Medical - Academic Torrents

DENTEX_CHALLENGE (Dataset)

@article{, title= {DENTEX_CHALLENGE}, keywords= {}, author= {}, abstract= {https://i.imgur.com/sAXITsB.png The DENTEX dataset comprises panoramic dental X-rays obtained from three different institutions using standard clinical conditions but varying equipment and imaging protocols, resulting in diverse image quality reflecting heterogeneous clinical practice. The dataset includes X-rays from patients aged 12 and above, randomly selected from the hospital's database to ensure patient privacy and confidentiality. To enable effective use of the FDI system, the dataset is hierarchically organized into three types of data; (a) 693 X-rays labeled for quadrant detection and quadrant classes only, (b) 634 X-rays labeled for tooth detection with quadrant and tooth enumeration classes, (c) 1005 X-rays fully labeled for abnormal tooth detection with quadrant, tooth enumeration, and diagnosis classes. The diagnosis class includes four specific categories: caries, deep caries, periapical lesions, and impacted teeth. An additional 1571 unlabeled X-rays are provided for pre-training. ## Data Split for Evaluation and Training The DENTEX 2023 dataset comprises three types of data: (a) partially annotated quadrant data, (b) partially annotated quadrant-enumeration data, and (c) fully annotated quadrant-enumeration-diagnosis data. The first two types of data are intended for training and development purposes, while the third type is used for training and evaluations. To comply with standard machine learning practices, the fully annotated third dataset, consisting of 1005 panoramic X-rays, is partitioned into training, validation, and testing subsets, comprising 705, 50, and 250 images, respectively. Ground truth labels are provided only for the training data, while the validation data is provided without associated ground truth, and the testing data is kept hidden from participants. Participants are allowed to use additional public data for augmenting the provided DENTEX dataset or for pre-training models on such datasets to enhance performance. However, they must ensure that all the data they use is publicly available. Additionally, they must document the use of external data clearly in their final short paper submission, providing details on the dataset and its source. ## Annotation Protocol The DENTEX provides three hierarchically annotated datasets that facilitate various dental detection tasks: (1) quadrant-only for quadrant detection, (2) quadrant-enumeration for tooth detection, and (3) quadrant-enumeration-diagnosis for abnormal tooth detection. Although it may seem redundant to provide a quadrant detection dataset, it is crucial for utilizing the FDI Numbering System. The FDI system is a globally-used system that assigns each quadrant of the mouth a number from 1 through 4. The top right is 1, the top left is 2, the bottom left is 3, and the bottom right is 4. Then each of the eight teeth and each molar are numbered 1 through 8. The 1 starts at the front middle tooth, and the numbers rise the farther back we go. So for example, the back tooth on the lower left side would be 48 according to FDI notation, which means quadrant 4, number 8. Therefore, the quadrant segmentation dataset can significantly simplify the dental enumeration task, even though evaluations will be made only on the fully annotated third data. All annotations in the DENTEX dataset are meticulously crafted by a team of dental experts. Specifically, each image is annotated by a last-year dental student, and the annotations are further verified and corrected by one of three expert dentists with over 15 years of experience. Therefore, the annotated data in DENTEX is of the highest quality and accuracy, which makes it a valuable resource for dental research.}, terms= {}, license= {}, superseded= {}, url= {https://dentex.grand-challenge.org/} }

VerSe'20 CT Dataset (Dataset)

@article{, title= {VerSe'20 CT Dataset}, keywords= {}, author= {}, abstract= {VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images ## What is VerSe? Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable. We believe *VerSe* can help here. VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation. ## Citing VerSe If you use VerSe, we would appreciate references to the following papers. 1. **Sekuboyina A et al., VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, 2021.**
In Medical Image Analysis: https://doi.org/10.1016/j.media.2021.102166
Pre-print: https://arxiv.org/abs/2001.09193 2. **Löffler M et al., A Vertebral Segmentation Dataset with Fracture Grading. Radiology: Artificial Intelligence, 2020.**
In Radiology AI: https://doi.org/10.1148/ryai.2020190138 3. **Liebl H and Schinz D et al., A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data, 2021.**
Pre-print: https://arxiv.org/pdf/2103.06360.pdf ## Data * The dataset has four files corresponding to one data sample: image, segmentation mask, centroid annotations, a PNG overview of the annotations. * Data structure - 01_training - Train data - 02_validation - (Formerly) PUBLIC test data - 03_test - (Formerly) HIDDEN test data * Sub-directory-based arrangement for each patient. File names are constructed of entities, a suffix and a file extension following the conventions of the Brain Imaging Data Structure (BIDS; https://bids.neuroimaging.io/) ``` Example: ------- training/rawdata/sub-verse000 sub-verse000_dir-orient_ct.nii.gz - CT image series training/derivatives/sub-verse000/ sub-verse000_dir-orient_seg-vert_msk.nii.gz - Segmentation mask of the vertebrae sub-verse000_dir-orient_seg-subreg_ctd.json - Centroid coordinates in image space sub-verse000_dir-orient_seg-vert_snp.png - Preview reformations of the annotated CT data. ``` * Centroid coordinates of the subject based structure (.json file) are given in voxels in the image space. 'label' corresponds to the vertebral label: - 1-7: cervical spine: C1-C7 - 8-19: thoracic spine: T1-T12 - 20-25: lumbar spine: L1-L6 - 26: sacrum - not labeled in this dataset - 27: cocygis - not labeled in this dataset - 28: additional 13th thoracic vertebra, T13}, terms= {}, license= {}, superseded= {}, url= {https://github.com/anjany/verse} }

Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation (Dataset)

@article{, title= {Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation}, keywords= {computed tomography}, author= {}, abstract= {AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research. https://zenodo.org/record/7155725}, terms= {}, license= {https://creativecommons.org/licenses/by/4.0/legalcode}, superseded= {}, url= {https://amos22.grand-challenge.org/} }

TotalSegmentator CT Dataset V2 (Dataset)

@article{, title= {TotalSegmentator CT Dataset V2}, keywords= {computed tomography, segmentation, segment}, author= {}, abstract= {Info: This is version 2 of the TotalSegmentator dataset. In 1228 CT images we segmented 117 anatomical structures covering a majority of relevant classes for most use cases. The CT images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. https://zenodo.org/record/8367088}, terms= {}, license= {https://creativecommons.org/licenses/by/4.0/}, superseded= {}, url= {https://github.com/wasserth/TotalSegmentator} }

SegThy Open-Access Dataset for Thyroid and Neck Segmentation (Dataset)

@article{, title= {SegThy Open-Access Dataset for Thyroid and Neck Segmentation}, keywords= {}, author= {Krönke, C. Eilers and D. Dimova, M. Köhler and G. Buschner, L. Schweiger and L. Konstantinidou and M. Makowski and J. Nagarajah and N. Navab and W. Weber and T. Wendler}, abstract= {## Motivation Ultrasound (US) imaging plays a central role in the diagnosis of thyroid diseases as well as different pathologies of the neck region. Additionally, US is used for treatment planning, in the case of radioiodine therapy, and also as mean of following up on the success of different therapeutic efforts. Yet, in the vast majority of hospitals, 2D freehand US is applied. This type of examination has shown to have a high intra-observer and inter-observer variability [1], and low accuracy in terms of volume prediction in a thyroid volumetry setting using MRI as ground truth. To tackle these problems, we propose the use of 3D US in combination with machine learning (ML) algorithms to automatically segment the most relevant organs in the region, as well as anomalies, such as nodules and tumors. 3D US can be acquired in several ways, e.g. using 3D US probes, robotic US, so-called wobbler US probes, or using freehand tracked US. The last option has gained traction over the last years as it enables to extend almost any existing commercial 2D US at a low cost. On the side of ML, its availability and increasing computing power are flooding the medical world. Yet, ML can only provide trustworthy results if sufficient (annotated) data is available to “learn” from it. This motivated us to create and publish this dataset, and thus make a relevant contribution to the community. ## Citation [1] Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry; M. Krönke, C. Eilers, D. Dimova, M. Köhler, G. Buschner, L. Schweiger, L. Konstantinidou, M. Makowski, J. Nagarajah, N. Navab, W. Weber, and T. Wendler; PLOS ONE - July 29, 2022 - https://doi.org/10.1371/journal.pone.0268550 ## Dataset Description Sub-dataset 1 This sub-dataset consists of 28 (healthy) volunteers who were scanned using freehand tracked ultrasonography (both sides of their neck, focus on thyroid). A Siemens Acuson NX-3 US machine, combined with a 12MHz VF12-4 probe, was adapted to be tracked using electromagnetic tracking using the Piur tUS system. Additionally, all patients were imaged with MRI (Siemens Biograph mMR) using a T1-weighted VIBE (volumetric interpolated breath-hold) sequence centered in the thyroid area of the neck. The magnetic field was set to 3T. In practical terms, each volunteer in this subdataset has: 1x MRI (T1-weighted VIBE sequence) of the neck area, with voxel size 0.625x0.625x1 mm3 and field of view 320x320x80 mm3. 9x two-sided 3D US covering the thyroid region with voxel size of 0.12x0.12x0.12 mm3 and variable field of view. The nine 3D US were acquired by three physicians (three scans each). Label maps for the thyroid in all MR and US images. Sub-dataset 2 The second subdataset consists of 186 patients undergoing routinary thyroid US either as initial diagnostics means or as follow-up. The same US device and freehand tracked US extension device were used. Each patient in this sub-dataset has 6x two-sided 3D US covering the thyroid. The 6 3D US were acquired by 2 physicians (three scans each). In both dataset, the age and the sex of the patients is included in the metadata. The dataset is currently being extended to include the labels of the trachea, the common carotid arteries, the internal jugular veins, and (if present) thyroid nodules in sub-dataset 1 (in both US and MRI), and of the same organs in sub-dataset 2 in US. See figures as examples for the multilabel annotations. ## License This dataset is licensed under a CC BY license. This means that users of it can distribute, shuffle, adapt, and build upon the material with the sole condition that the creators are acknowledged. The license permits commercial use as long as the authors are cited.}, terms= {}, license= {https://creativecommons.org/licenses/by/4.0/}, superseded= {}, url= {https://www.cs.cit.tum.de/camp/publications/segthy-dataset/} }

CAMUS Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (Dataset)

@article{, title= {CAMUS Cardiac Acquisitions for Multi-structure Ultrasound Segmentation}, keywords= {}, author= {}, abstract= {The goal of this project is to provide all the materials to the community to resolve the problem of echocardiographic image segmentation and volume estimation from 2D ultrasound sequences (both two and four-chamber views). To this aim, the following solutions were set-up introduction of the largest publicly-available and fully-annotated dataset for 2D echocardiographic assessment (to our knowledge). The CAMUS dataset, containing 2D apical four-chamber and two-chamber view sequences acquired from 500 patients, is made available for download # Dataset properties The overall CAMUS dataset consists of clinical exams from 500 patients, acquired at the University Hospital of St Etienne (France) and included in this study within the regulation set by the local ethical committee of the hospital after full anonymization. The acquisitions were optimized to perform left ventricle ejection fraction measurements. In order to enforce clinical realism, neither prerequisite nor data selection have been performed. Consequently, - some cases were difficult to trace; - the dataset involves a wide variability of acquisition settings; - for some patients, parts of the wall were not visible in the images; for some cases, the probe orientation recommendation to acquire a rigorous four-chambers view was simply impossible to follow and a five-chambers view was acquired instead. This produced a highly heterogeneous dataset, both in terms of image quality and pathological cases, which is typical of daily clinical practice data. The dataset has been made available to the community HERE. The dataset comprises : i) a training set of 450 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing set composed of 50 new patients. The raw input images are provided through the raw/mhd file format. # Study population Half of the dataset population has a left ventricle ejection fraction lower than 45%, thus being considered at pathological risk (beyond the uncertainty of the measurement). Also, 19% of the images have a poor quality (based on the opinion of one expert), indicating that for this subgroup the localization of the left ventricle endocarium and left ventricle epicardium as well as the estimation of clinical indices are not considered clinically accurate and workable. In classical analysis, poor quality images are usually removed from the dataset because of their clinical uselessness. Therefore, those data were not involved in this project during the computation of the different metrics but were used to study their influence as part of the training and validation sets for deep learning techniques. # Involved systems The full dataset was acquired from GE Vivid E95 ultrasound scanners (GE Vingmed Ultrasound, Horten Norway), with a GE M5S probe (GE Healthcare, US). No additional protocol than the one used in clinical routine was put in place. For each patient, 2D apical four-chamber and two-chamber view sequences were exported from EchoPAC analysis software (GE Vingmed Ultrasound, Horten, Norway). These standard cardiac views were chosen for this study to enable the estimation of left ventricle ejection fraction values based on the Simpson’s biplane method of discs. Each exported sequence corresponds to a set of B-mode images expressed in polar coordinates. The same interpolation procedure was used to express all sequences in Cartesian coordinates with a unique grid resolution, i.e. λ/2 = 0.3 mm along the x-axis (axis parallel to the probe) and λ/4 = 0.15 mm along the z-axis (axis perpendicular to the probe), where λ corresponds to the wavelength of the ultrasound probe. At least one full cardiac cycle was acquired for each patient in each view, allowing manual annotation of cardiac structures at ED and ES. This work has published to IEEE TMI journal. You must cite this paper for any use of the CAMUS database ``` S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al. "Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198-2210, Sept. 2019. ``` The CAMUS dataset is free of charge under [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) (Attribution-NonCommercial-ShareAlike). Participants must agree to comply with the terms of CC BY-NC-SA 4.0, and also agree to the specific terms: 1) The CAMUS dataset is available as free to use strictly only non-commercial scientific research purposes 2) Participants must use the following citation when referencing the CAMUS dataset. S. Leclerc, E. Smistad, J. Pedrosa, A. Ostvik, et al. "Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography" in IEEE Transactions on Medical Imaging, vol. 38, no. 9, pp. 2198-2210, Sept. 2019 doi: 10.1109/TMI.2019.2900516 https://humanheart-project.creatis.insa-lyon.fr/database/#collection/6373703d73e9f0047faa1bc8/folder/66e27d12961576b1bad4e4e1 https://i.imgur.com/aVBYSWH.jpg}, terms= {}, license= {https://creativecommons.org/licenses/by-nc-sa/4.0/}, superseded= {}, url= {https://www.creatis.insa-lyon.fr/Challenge/camus/} }

HMC-QU echocardiography ultrasound recordings (Dataset)

@article{, title= {HMC-QU echocardiography ultrasound recordings}, keywords= {ultrasound}, author= {}, abstract= {The HMC-QU benchmark dataset is created by the collaboration between Hamad Medical Corporation (HMC), Tampere University, and Qatar University. The usage of data has been approved by the local ethics board of HMC Hospital in February 2019. The dataset includes a collection of apical 4-chamber (A4C) and apical 2-chamber (A2C) view 2D echocardiography recordings obtained during the years 2018 and 2019. The echocardiography recordings are acquired via devices from different vendors that are Phillips and GE Vivid (GE-Health-USA) ultrasound machines. The temporal resolution (frame rate per second) of the echocardiography recordings is 25 fps. The spatial resolution varies from 422x636 to 768x1024 pixels. The dataset can be utilized for both myocardial infarction (heart attack) detection and left ventricle wall segmentation purposes. # Detection of Myocardial Infarction HMC-QU is the first dataset that is shared with the research community serving myocardial infarction (MI) detection on the left ventricle wall of the heart. The recordings are from over 10,000 echos performed in a year including more than 800 cases admitted with acute ST-elevation MI. The patients with MI were treated with coronary angiogram/angioplasty after the diagnosis of acute MI with electrocardiography and cardiac enzymes evidence. The patients had echocardiography recordings obtained within 24 hours of admission or in some cases before they underwent coronary angioplasty. The subjects not diagnosed with MI underwent a required health check and investigation for other reasons in the hospital. The ground-truth labels are provided for each myocardial segment illustrated in Figure 1 as non-MI and MI, where the MI term indicates any sign of regional wall motion abnormality, whereas the subjects without regional wall motion abnormality are assigned to non-MI. The one cardiac cycle frames are predefined for each recording. End-diastole and end-systole frames are defined according to the electrocardiography (ECG) recordings of the patients. For the patients without ECG recordings, the cardiac cycle is defined according to the frames, where the left ventricle area is the largest and smallest. 1.1. Apical 4-chamber HMC-QU dataset consists of 162 A4C view 2D echocardiography recordings. The A4C view recordings belong to 93 MI patients (all first-time and acute MI) and 69 non-MI subjects. 1.2. Apical 2-chamber The dataset consists of 130 A2C view 2D echocardiography recordings that belong to 68 MI patients and 62 non-MI subjects. # Segmentation of the Left Ventricle Wall A subset of 109 A4C view echocardiography recordings has their corresponding ground-truth segmentation masks for the whole left ventricle wall at each frame for one cardiac cycle. This subset includes 72 MI patients and 37 non-MI subjects. The size of the ground-truth segmentation masks is 224x224 in order to have suitable input dimensions for many state-of-the-art deep network topologies. If you use the HMC-QU dataset in your research, please consider citing the publications below: [P1] A. Degerli, S. Kiranyaz, T. Hamid, R. Mazhar, and M. Gabbouj, “Early Myocardial Infarction Detection over Multi-view Echocardiography,” arXiv preprint arXiv:2111.05790v2, 2021, https://doi.org/10.48550/arXiv.2111.05790. [P2] A. Degerli, M. Zabihi, S. Kiranyaz, T. Hamid, R. Mazhar, R. Hamila, and M. Gabbouj, "Early Detection of Myocardial Infarction in Low-Quality Echocardiography," in IEEE Access, vol. 9, pp. 34442-34453, 2021, https://doi.org/10.1109/ACCESS.2021.3059595. [P3] S. Kiranyaz, A. Degerli, T. Hamid, R. Mazhar, R. E. F. Ahmed, R. Abouhasera, M. Zabihi, J. Malik, R. Hamila, and M. Gabbouj, "Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection," in IEEE Access, vol. 8, pp. 210301-210317, 2020, https://doi.org/10.1109/ACCESS.2020.3038743. https://i.imgur.com/QKsdWPb.jpg}, terms= {}, license= {https://creativecommons.org/licenses/by-nc-sa/3.0/igo/}, superseded= {}, url= {https://www.kaggle.com/datasets/aysendegerli/hmcqu-dataset} }

STructured Analysis of the Retina (Dataset)

@article{, title= {STructured Analysis of the Retina}, keywords= {}, author= {}, abstract= {The STARE (STructured Analysis of the Retina) Project was conceived and initiated in 1975 by Michael Goldbaum, M.D., at the University of California, San Diego. It was funded by the U.S. National Institutes of Health . During its history, over thirty people contributed to the project, with backgrounds ranging from medicine to science to engineering. Images and clinical data were provided by the Shiley Eye Center at the University of California, San Diego, and by the Veterans Administration Medical Center in San Diego. I had the pleasure of working on the project from 1996-2004. The contents of this web page reflect my contributions. Please contact me if you have any questions or requests concerning our data or code. Please contact Dr. Goldbaum if you have any requests concerning the current state of the project. # A brief overview of the project An ophthalmologist is a medical doctor that specializes in the structure, function, and diseases of the human eye. During a clinical examination, an opthalmologist notes findings that are visible in the eyes of the subject. The ophthalmologist then uses these findings to reason about the health of the subject. For instance, a patient may exhibit discoloration of the optic nerve, or a narrowing of the blood vessels in the retina. An opthalmologist uses this information to diagnose the patient, as having for instance Coats' disease or a central retinal artery occlusion. A common procedure during an examination is retinal imaging. An optical camera is used to see through the pupil of the eye to the rear inner surface of the eyeball. A picture is taken showing the optic nerve, fovea, surrounding vessels, and the retinal layer. The opthalmologist can then reference this image while considering any observed findings. This research concerns a system to automatically diagnose diseases of the human eye. The system takes as input information observable in a retinal image. This information is formulated to mimic the findings that an ophthalmologist would note during a clinical examination. The main output of the system is a diagnosis formulated to mimic the conclusion that an ophthalmologist would reach about the health of the subject. Our approach breaks the problem into two components. The first component concerns automatically processing a retinal image to denote the important findings. The second component concerns automatically reasoning about the findings to determine a diagnosis. Additional outputs include detailed measurements of the anatomical structures and lesions visible in the retinal image. These measurements are useful for tracking disease severity and the evaluation of treatment progress over time. By collecting a database of measurements for a large number of people, the STARE project could support clinical population studies and intern training. https://i.imgur.com/rMBvdYq.jpg # Papers A lot has been published on this project by many people; these are my two most relevant papers: A. Hoover, V. Kouznetsova and M. Goldbaum, "Locating Blood Vessels in Retinal Images by Piece-wise Threhsold Probing of a Matched Filter Response", IEEE Transactions on Medical Imaging , vol. 19 no. 3, pp. 203-210, March 2000. A. Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels", IEEE Transactions on Medical Imaging , vol. 22 no. 8, pp. 951-958, August 2003. }, terms= {}, license= {}, superseded= {}, url= {https://cecas.clemson.edu/~ahoover/stare/} }

Data of the White Matter Hyperintensity (WMH) Segmentation Challenge (Dataset)

@article{, title= {Data of the White Matter Hyperintensity (WMH) Segmentation Challenge}, keywords= {}, author= {Kuijf, Hugo and Biesbroek, Matthijs and de Bresser, Jeroen and Heinen, Rutger and Chen, Christopher and van der Flier, Wiesje and Barkhof and Viergever, Max and Biessels, Geert Jan}, abstract= {Data of the WMH Segmentation Challenge, including the training data, test data, manual annotations, and additional manual annotations. Contents: - readme.pdf - training: contains all training data that was originally released - test: contains all test data - additional_annotations: contains additional manual annotations of two extra observers Code: https://github.com/hjkuijf/wmhchallenge https://wmh.isi.uu.nl/ https://i.imgur.com/RJjPBbP.png}, terms= {}, license= {http://creativecommons.org/licenses/by-nc/4.0}, superseded= {}, url= {https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/AECRSD} }

TotalSegmentator CT Dataset (Dataset)

@article{, title= {TotalSegmentator CT Dataset}, keywords= {segmenter, segmentation, computed tomography, segment}, author= {Department of Research and Analysis at University Hospital Basel.}, abstract= {https://i.imgur.com/u63xva0.png In 1204 CT images we segmented 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) covering a majority of relevant classes for most use cases. The CT images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. ``` s0720/segmentations/portal_vein_and_splenic_vein.nii.gz187.74kB s0720/segmentations/pancreas.nii.gz45.25kB s0720/segmentations/lung_upper_lobe_right.nii.gz218.92kB s0720/segmentations/lung_upper_lobe_left.nii.gz230.82kB s0720/segmentations/lung_middle_lobe_right.nii.gz201.18kB s0720/segmentations/lung_lower_lobe_right.nii.gz240.63kB s0720/segmentations/lung_lower_lobe_left.nii.gz239.49kB s0720/segmentations/liver.nii.gz273.08kB s0720/segmentations/kidney_right.nii.gz198.91kB s0720/segmentations/kidney_left.nii.gz197.82kB s0720/segmentations/inferior_vena_cava.nii.gz48.43kB s0720/segmentations/iliopsoas_right.nii.gz59.12kB s0720/segmentations/iliopsoas_left.nii.gz59.75kB s0720/segmentations/iliac_vena_right.nii.gz188.90kB s0720/segmentations/iliac_vena_left.nii.gz189.66kB s0720/segmentations/iliac_artery_right.nii.gz186.75kB s0720/segmentations/iliac_artery_left.nii.gz186.60kB s0720/segmentations/humerus_right.nii.gz41.96kB s0720/segmentations/humerus_left.nii.gz43.13kB s0720/segmentations/hip_right.nii.gz223.33kB s0720/segmentations/hip_left.nii.gz223.06kB s0720/segmentations/heart_ventricle_right.nii.gz48.07kB s0720/segmentations/heart_ventricle_left.nii.gz45.10kB s0720/segmentations/heart_myocardium.nii.gz49.17kB s0720/segmentations/heart_atrium_right.nii.gz44.41kB s0720/segmentations/heart_atrium_left.nii.gz43.02kB s0720/segmentations/gluteus_minimus_right.nii.gz46.65kB s0720/segmentations/gluteus_minimus_left.nii.gz45.95kB s0720/segmentations/gluteus_medius_right.nii.gz53.75kB s0720/segmentations/gluteus_medius_left.nii.gz52.68kB s0720/segmentations/gluteus_maximus_right.nii.gz58.02kB s0720/segmentations/gluteus_maximus_left.nii.gz56.20kB s0720/segmentations/gallbladder.nii.gz42.20kB s0720/segmentations/femur_right.nii.gz192.93kB s0720/segmentations/femur_left.nii.gz193.47kB s0720/segmentations/face.nii.gz183.15kB s0720/segmentations/esophagus.nii.gz188.93kB s0720/segmentations/duodenum.nii.gz189.53kB s0720/segmentations/colon.nii.gz239.38kB s0720/segmentations/clavicula_right.nii.gz42.92kB s0720/segmentations/clavicula_left.nii.gz42.50kB s0720/segmentations/brain.nii.gz183.15kB s0720/segmentations/autochthon_right.nii.gz62.97kB s0720/segmentations/autochthon_left.nii.gz63.75kB s0720/segmentations/aorta.nii.gz202.39kB s0720/segmentations/adrenal_gland_right.nii.gz184.50kB s0720/segmentations/adrenal_gland_left.nii.gz184.35kB s0720/ct.nii.gz ``` https://arxiv.org/abs/2208.05868 https://zenodo.org/record/6802614}, terms= {}, license= {https://creativecommons.org/licenses/by/4.0}, superseded= {}, url= {https://arxiv.org/abs/2208.05868} }

Breast Ultrasound Images Dataset (Dataset BUSI) (Dataset)

@article{, title= {Breast Ultrasound Images Dataset (Dataset BUSI)}, keywords= {}, author= {}, abstract= {The data collected at baseline include breast ultrasound images among women in ages between 25 and 75 years old. This data was collected in 2018. The number of patients is 600 female patients. The dataset consists of 780 images with an average image size of 500*500 pixels. The images are in PNG format. The ground truth images are presented with original images. The images are categorized into three classes, which are normal, benign, and malignant. If you use this dataset, please cite: Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863. | Subject area | Medicine and Dentistry | |----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | More specific subject area | Radiology and Imaging | | Type of data | Images and mask images | | How data was acquired | LOGIQ E9 ultrasound and LOGIQ E9 Agile ultrasound system | | Data format | PNG | | Experimental factors | All images are classified as normal, benign and malignant | | Experimental features | When medical images are used for training deep learning models, they provide fast and accurate results in classification, detection, and segmentation of breast cancer. | | Data source location | Baheya Hospital for Early Detection & Treatment of Women's Cancer, Cairo, Egypt. | | Data accessibility | https://scholar.cu.edu.eg/?q=afahmy/pages/dataset | | Related research article | 1. Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled and Aly Fahmy, Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images [1] | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906728/ https://i.imgur.com/WV1Tfb7.png}, terms= {}, license= {}, superseded= {}, url= {https://scholar.cu.edu.eg/?q=afahmy/pages/dataset} }

TB Portal Tuberculosis Chest X-ray dataset for Belarus (Dataset)

@article{, title= {TB Portal Tuberculosis Chest X-ray dataset for Belarus}, keywords= {}, author= {}, abstract= {This is a tuberculosis Chest X-ray dataset containing patients who are resistant to conventional tuberculosis treatment. Data is provided in raw format as available in https://tbportals.niaid.nih.gov. The dataset mainly comes from the population of Belarus - in total over 1000 tuberculosis cases are provided. Credits to: TB Portals Program, Office of Cyber Infrastructure and Computational Biology (OCICB), National Institute of Allergy and Infectious Diseases (NIAID).}, terms= {}, license= {}, superseded= {}, url= {https://www.kaggle.com/raddar/drug-resistant-tuberculosis-xrays} }

PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification (Dataset)

@article{, title= {PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification}, keywords= {}, author= {Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir}, abstract= {https://i.imgur.com/iYlXSCm.png Semi automatically generated nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. The dataset consists of 481 visual fields, of which 312 are randomly sampled from more than 20K whole slide images at different magnifications, from multiple data sources. In total the dataset contains 205,343 labeled nuclei, each with an instance segmentation mask. Models trained on pannuke can aid in whole slide image tissue type segmentation, and generalise to new tissues. PanNuke demonstrates one of the first succesfully semi-automatically generated datasets. ## citation ``` @inproceedings{gamper2019pannuke, title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification}, author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benet, Ksenija and Khuram, Ali and Rajpoot, Nasir}, booktitle={European Congress on Digital Pathology}, pages={11--19}, year={2019}, organization={Springer} } @article{gamper2020pannuke, title={PanNuke Dataset Extension, Insights and Baselines}, author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir}, journal={arXiv preprint arXiv:2003.10778}, year={2020} } ``` https://i.imgur.com/T4ogyHR.png}, terms= {}, license= {http://creativecommons.org/licenses/by-nc-sa/4.0/}, superseded= {}, url= {https://jgamper.github.io/PanNukeDataset/} }

DRIVE: Digital Retinal Images for Vessel Extraction (Dataset)

@article{, title= {DRIVE: Digital Retinal Images for Vessel Extraction}, keywords= {}, author= {}, abstract= {The DRIVE database has been established to enable comparative studies on segmentation of blood vessels in retinal images. Retinal vessel segmentation and delineation of morphological attributes of retinal blood vessels, such as length, width, tortuosity, branching patterns and angles are utilized for the diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension, arteriosclerosis and chorodial neovascularization. Automatic detection and analysis of the vasculature can assist in the implementation of screening programs for diabetic retinopathy, can aid research on the relationship between vessel tortuosity and hypertensive retinopathy, vessel diameter measurement in relation with diagnosis of hypertension, and computer-assisted laser surgery. Automatic generation of retinal maps and extraction of branch points have been used for temporal or multimodal image registration and retinal image mosaic synthesis. Moreover, the retinal vascular tree is found to be unique for each individual and can be used for biometric identification. ## Data The photographs for the DRIVE database were obtained from a diabetic retinopathy screening program in The Netherlands. The screening population consisted of 400 diabetic subjects between 25-90 years of age. Forty photographs have been randomly selected, 33 do not show any sign of diabetic retinopathy and 7 show signs of mild early diabetic retinopathy. Here is a brief description of the abnormalities in these 7 cases: 25_training: pigment epithelium changes, probably butterfly maculopathy with pigmented scar in fovea, or choroidiopathy, no diabetic retinopathy or other vascular abnormalities. 26_training: background diabetic retinopathy, pigmentary epithelial atrophy, atrophy around optic disk 32_training: background diabetic retinopathy 03_test: background diabetic retinopathy 08_test: pigment epithelium changes, pigmented scar in fovea, or choroidiopathy, no diabetic retinopathy or other vascular abnormalities 14_test: background diabetic retinopathy 17_test: background diabetic retinopathy Each image has been JPEG compressed. The images were acquired using a Canon CR5 non-mydriatic 3CCD camera with a 45 degree field of view (FOV). Each image was captured using 8 bits per color plane at 768 by 584 pixels. The FOV of each image is circular with a diameter of approximately 540 pixels. For this database, the images have been cropped around the FOV. For each image, a mask image is provided that delineates the FOV. The set of 40 images has been divided into a training and a test set, both containing 20 images. For the training images, a single manual segmentation of the vasculature is available. For the test cases, two manual segmentations are available; one is used as gold standard, the other one can be used to compare computer generated segmentations with those of an independent human observer. Furthermore, a mask image is available for every retinal image, indicating the region of interest. All human observers that manually segmented the vasculature were instructed and trained by an experienced ophthalmologist. They were asked to mark all pixels for which they were for at least 70% certain that they were vessel. https://i.imgur.com/AkjZ5pz.png}, terms= {}, license= {}, superseded= {}, url= {https://drive.grand-challenge.org/} }

Object-CXR - Automatic detection of foreign objects on chest X-rays (Dataset)

@article{, title= {Object-CXR - Automatic detection of foreign objects on chest X-rays}, keywords= {radiology}, author= {JF Healthcare}, abstract= {## Data 5000 frontal chest X-ray images with foreign objects presented and 5000 frontal chest X-ray images without foreign objects were filmed and collected from about 300 township hosiptials in China. 12 medically-trained radiologists with 1 to 3 years of experience annotated all the images. Each annotator manually annotates the potential foreign objects on a given chest X-ray presented within the lung field. Foreign objects were annotated with bounding boxes, bounding ellipses or masks depending on the shape of the objects. Support devices were excluded from annotation. A typical frontal chest X-ray with foreign objects annotated looks like this: https://i.imgur.com/SFUZy80.jpg ## Annotation Object-level annotations for each image, which indicate the rough location of each foreign object using a closed shape. Annotations are provided in csv files and a csv example is shown below. ```csv image_path,annotation /path/#####.jpg,ANNO_TYPE_IDX x1 y1 x2 y2;ANNO_TYPE_IDX x1 y1 x2 y2 ... xn yn;... /path/#####.jpg, /path/#####.jpg,ANNO_TYPE_IDX x1 y1 x2 y2 ... ``` Three type of shapes are used namely rectangle, ellipse and polygon. We use `0`, `1` and `2` as `ANNO_TYPE_IDX` respectively. - For rectangle and ellipse annotations, we provide the bounding box (upper left and lower right) coordinates in the format `x1 y1 x2 y2` where `x1` < `x2` and `y1` < `y2`. - For polygon annotations, we provide a sequence of coordinates in the format `x1 y1 x2 y2 ... xn yn`. > ### Note: > Our annotations use a Cartesian pixel coordinate system, with the origin (0,0) in the upper left corner. The x coordinate extends from left to right; the y coordinate extends downward. ## Organizers [JF Healthcare](http://www.jfhealthcare.com/) is the primary organizer of this challenge. }, terms= {}, license= {https://creativecommons.org/licenses/by-nc/4.0/}, superseded= {}, url= {https://web.archive.org/web/20201127235812/https://jfhealthcare.github.io/object-CXR/} }

SIIM-ACR Pneumothorax Segmentation (Dataset)

@article{, title= {SIIM-ACR Pneumothorax Segmentation}, keywords= {radiology}, author= {Society for Imaging Informatics in Medicine (SIIM)}, abstract= {In this competition, you’ll develop a model to classify (and if present, segment) pneumothorax from a set of chest radiographic images. If successful, you could aid in the early recognition of pneumothoraces and save lives. What am I predicting? We are attempting to a) predict the existence of pneumothorax in our test images and b) indicate the location and extent of the condition using masks. Your model should create binary masks and encode them using RLE. https://i.imgur.com/xJYwEv4.png}, terms= {}, license= {}, superseded= {}, url= {https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation} }

PMC Open Access Subset (Dataset)

@article{, title= {PMC Open Access Subset}, journal= {}, author= {NIH/NLM}, year= {}, url= {https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/}, abstract= {https://i.imgur.com/GBSDr8v.png mirror of ftp.ncbi.nlm.nih.gov:/pub/pmc/oa_bulk PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). https://www.ncbi.nlm.nih.gov/pmc/ The PMC Open Access Subset some or all openaccess content is a part of the total collection of articles in PMC. The articles in the OA Subset are made available under a Creative Commons or similar license that generally allows more liberal redistribution and reuse than a traditional copyrighted work. https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/}, keywords= {PMC, PubMed Central}, terms= {}, license= {CC}, superseded= {} }

MICCAI_BraTS_2019_Data_Training (Dataset)

@article{, title= {MICCAI_BraTS_2019_Data_Training}, keywords= {}, author= {BraTS}, abstract= {https://i.imgur.com/iONFbKt.gif Volume list (count: 335) ``` HGG/BraTS19_2013_11_1 HGG/BraTS19_2013_12_1 HGG/BraTS19_2013_13_1 HGG/BraTS19_2013_14_1 HGG/BraTS19_2013_17_1 HGG/BraTS19_2013_18_1 HGG/BraTS19_2013_19_1 HGG/BraTS19_2013_20_1 HGG/BraTS19_2013_2_1 HGG/BraTS19_2013_22_1 HGG/BraTS19_2013_23_1 HGG/BraTS19_2013_25_1 HGG/BraTS19_2013_26_1 HGG/BraTS19_2013_27_1 HGG/BraTS19_2013_3_1 HGG/BraTS19_2013_21_1 HGG/BraTS19_2013_4_1 HGG/BraTS19_2013_5_1 HGG/BraTS19_2013_7_1 HGG/BraTS19_CBICA_AAB_1 HGG/BraTS19_CBICA_AAG_1 HGG/BraTS19_CBICA_AAL_1 HGG/BraTS19_CBICA_AAP_1 HGG/BraTS19_CBICA_ABB_1 HGG/BraTS19_CBICA_ABE_1 HGG/BraTS19_CBICA_ABM_1 HGG/BraTS19_CBICA_ABN_1 HGG/BraTS19_CBICA_ABO_1 HGG/BraTS19_CBICA_ABY_1 HGG/BraTS19_CBICA_ALN_1 HGG/BraTS19_CBICA_ALU_1 HGG/BraTS19_CBICA_ALX_1 HGG/BraTS19_CBICA_AME_1 HGG/BraTS19_CBICA_AMH_1 HGG/BraTS19_CBICA_ANG_1 HGG/BraTS19_CBICA_ANI_1 HGG/BraTS19_CBICA_ANP_1 HGG/BraTS19_CBICA_ANZ_1 HGG/BraTS19_CBICA_AOD_1 HGG/BraTS19_CBICA_AOH_1 HGG/BraTS19_CBICA_AOO_1 HGG/BraTS19_CBICA_AOP_1 HGG/BraTS19_CBICA_AOZ_1 HGG/BraTS19_CBICA_APR_1 HGG/BraTS19_CBICA_APY_1 HGG/BraTS19_CBICA_APZ_1 HGG/BraTS19_CBICA_AQA_1 HGG/BraTS19_CBICA_AQD_1 HGG/BraTS19_CBICA_AQG_1 HGG/BraTS19_CBICA_AQJ_1 HGG/BraTS19_CBICA_AQN_1 HGG/BraTS19_CBICA_AQO_1 HGG/BraTS19_CBICA_AQP_1 HGG/BraTS19_CBICA_AQQ_1 HGG/BraTS19_CBICA_AQR_1 HGG/BraTS19_CBICA_AQT_1 HGG/BraTS19_CBICA_AQU_1 HGG/BraTS19_CBICA_AQV_1 HGG/BraTS19_CBICA_AQY_1 HGG/BraTS19_CBICA_AQZ_1 HGG/BraTS19_CBICA_ARF_1 HGG/BraTS19_CBICA_ARW_1 HGG/BraTS19_CBICA_ARZ_1 HGG/BraTS19_CBICA_ASA_1 HGG/BraTS19_CBICA_ASE_1 HGG/BraTS19_CBICA_ASG_1 HGG/BraTS19_CBICA_ASH_1 HGG/BraTS19_CBICA_ASK_1 HGG/BraTS19_CBICA_ASN_1 HGG/BraTS19_CBICA_ASO_1 HGG/BraTS19_CBICA_ASU_1 HGG/BraTS19_CBICA_ASV_1 HGG/BraTS19_CBICA_ASW_1 HGG/BraTS19_CBICA_ASY_1 HGG/BraTS19_CBICA_ATB_1 HGG/BraTS19_CBICA_ATD_1 HGG/BraTS19_CBICA_ATF_1 HGG/BraTS19_CBICA_ATP_1 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LGG/BraTS19_TCIA10_175_1 LGG/BraTS19_TCIA10_202_1 LGG/BraTS19_TCIA10_241_1 LGG/BraTS19_TCIA10_261_1 LGG/BraTS19_TCIA10_266_1 LGG/BraTS19_TCIA10_276_1 LGG/BraTS19_TCIA10_282_1 LGG/BraTS19_TCIA10_299_1 LGG/BraTS19_TCIA10_307_1 LGG/BraTS19_TCIA10_310_1 LGG/BraTS19_TCIA10_325_1 LGG/BraTS19_TCIA10_330_1 LGG/BraTS19_TCIA10_346_1 LGG/BraTS19_TCIA10_351_1 LGG/BraTS19_TCIA10_387_1 LGG/BraTS19_TCIA10_393_1 LGG/BraTS19_TCIA10_408_1 LGG/BraTS19_TCIA10_410_1 LGG/BraTS19_TCIA10_413_1 LGG/BraTS19_TCIA10_420_1 LGG/BraTS19_TCIA10_442_1 LGG/BraTS19_TCIA10_449_1 LGG/BraTS19_TCIA10_490_1 LGG/BraTS19_TCIA10_625_1 LGG/BraTS19_TCIA10_628_1 LGG/BraTS19_TCIA10_629_1 LGG/BraTS19_TCIA10_632_1 LGG/BraTS19_TCIA10_637_1 LGG/BraTS19_TCIA10_639_1 LGG/BraTS19_TCIA10_640_1 LGG/BraTS19_TCIA10_644_1 LGG/BraTS19_TCIA12_101_1 LGG/BraTS19_TCIA12_249_1 LGG/BraTS19_TCIA12_298_1 LGG/BraTS19_TCIA12_466_1 LGG/BraTS19_TCIA12_470_1 LGG/BraTS19_TCIA12_480_1 LGG/BraTS19_TCIA13_615_1 LGG/BraTS19_TCIA13_618_1 LGG/BraTS19_TCIA13_621_1 LGG/BraTS19_TCIA13_623_1 LGG/BraTS19_TCIA13_624_1 LGG/BraTS19_TCIA13_630_1 LGG/BraTS19_TCIA13_633_1 LGG/BraTS19_TCIA13_634_1 LGG/BraTS19_TCIA13_642_1 LGG/BraTS19_TCIA13_645_1 LGG/BraTS19_TCIA13_650_1 LGG/BraTS19_TCIA13_653_1 LGG/BraTS19_TCIA13_654_1 LGG/BraTS19_TMC_09043_1 LGG/BraTS19_2013_0_1 LGG/BraTS19_TCIA09_141_1 ```}, terms= {}, license= {}, superseded= {}, url= {https://www.med.upenn.edu/cbica/brats2019.html} }

RSNA Pneumonia Detection Challenge (JPG files) (Dataset)

@article{, title= {RSNA Pneumonia Detection Challenge (JPG files)}, keywords= {}, author= {}, abstract= {Details from the challenge: ## What am I predicting? In this challenge competitors are predicting whether pneumonia exists in a given image. They do so by predicting bounding boxes around areas of the lung. Samples without bounding boxes are negative and contain no definitive evidence of pneumonia. Samples with bounding boxes indicate evidence of pneumonia. When making predictions, competitors should predict as many bounding boxes as they feel are necessary, in the format: confidence x-min y-min width height There should be only ONE predicted row per image. This row may include multiple bounding boxes. A properly formatted row may look like any of the following. For patientIds with no predicted pneumonia / bounding boxes: 0004cfab-14fd-4e49-80ba-63a80b6bddd6, For patientIds with a single predicted bounding box: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,0.5 0 0 100 100 For patientIds with multiple predicted bounding boxes: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,0.5 0 0 100 100 0.5 0 0 100 100, etc. ## File descriptions ``` stage_2_train.csv - the training set. Contains patientIds and bounding box / target information. stage_2_detailed_class_info.csv - provides detailed information about the type of positive or negative class for each image. ``` ## Data fields ``` patientId _- A patientId. Each patientId corresponds to a unique image. x_ - the upper-left x coordinate of the bounding box. y_ - the upper-left y coordinate of the bounding box. width_ - the width of the bounding box. height_ - the height of the bounding box. Target_ - the binary Target, indicating whether this sample has evidence of pneumonia. ```}, terms= {}, license= {}, superseded= {}, url= {https://www.kaggle.com/c/rsna-pneumonia-detection-challenge} }

RSNA Pneumonia Detection Challenge (DICOM files) (Dataset)

@article{, title= {RSNA Pneumonia Detection Challenge (DICOM files)}, keywords= {}, author= {}, abstract= {Details from the challenge: ## What am I predicting? In this challenge competitors are predicting whether pneumonia exists in a given image. They do so by predicting bounding boxes around areas of the lung. Samples without bounding boxes are negative and contain no definitive evidence of pneumonia. Samples with bounding boxes indicate evidence of pneumonia. When making predictions, competitors should predict as many bounding boxes as they feel are necessary, in the format: confidence x-min y-min width height There should be only ONE predicted row per image. This row may include multiple bounding boxes. A properly formatted row may look like any of the following. For patientIds with no predicted pneumonia / bounding boxes: 0004cfab-14fd-4e49-80ba-63a80b6bddd6, For patientIds with a single predicted bounding box: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,0.5 0 0 100 100 For patientIds with multiple predicted bounding boxes: 0004cfab-14fd-4e49-80ba-63a80b6bddd6,0.5 0 0 100 100 0.5 0 0 100 100, etc. ## File descriptions ``` stage_2_train.csv - the training set. Contains patientIds and bounding box / target information. stage_2_detailed_class_info.csv - provides detailed information about the type of positive or negative class for each image. ``` ## Data fields ``` patientId _- A patientId. Each patientId corresponds to a unique image. x_ - the upper-left x coordinate of the bounding box. y_ - the upper-left y coordinate of the bounding box. width_ - the width of the bounding box. height_ - the height of the bounding box. Target_ - the binary Target, indicating whether this sample has evidence of pneumonia. ```}, terms= {}, license= {}, superseded= {}, url= {https://www.kaggle.com/c/rsna-pneumonia-detection-challenge} }

Pediatric Chest X-ray Pneumonia (Bacterial vs Viral vs Normal) Dataset (Dataset)

@article{, title= {Pediatric Chest X-ray Pneumonia (Bacterial vs Viral vs Normal) Dataset}, keywords= {}, author= {Kermany, Daniel S. et al.}, abstract= {The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care. For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert. https://i.imgur.com/zRBD2Js.png Figure S6. Illustrative Examples of Chest X-Rays in Patients with Pneumonia, Related to Figure 6 The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 ## Acknowledgements Data: https://data.mendeley.com/datasets/rscbjbr9sj/2 License: CC BY 4.0 Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 }, terms= {}, license= {https://creativecommons.org/licenses/by/4.0/}, superseded= {}, url= {http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5} }

LC25000 Lung and colon histopathological image dataset (Dataset)

@article{, title= {LC25000 Lung and colon histopathological image dataset}, keywords= {}, author= {Andrew A. Borkowski and Marilyn M. Bui and L. Brannon Thomas and Catherine P. Wilson and Lauren A. DeLand and Stephen M. Mastorides}, abstract= {LC25000 LUNG AND COLON HISTOPATHOLOGICAL IMAGE DATASET The dataset contains color 25,000 images with 5 classes of 5,000 images each. All images are 768 x 768 pixels in size and are in jpeg file format. Our dataset can be downloaded as a 1.85 GB zip file LC25000.zip. After unzipping, the main folder lung_colon_image_set contains two subfolders: colon_image_sets and lung_image_sets. The subfolder colon_image_sets contains two secondary subfolders: colon_aca subfolder with 5000 images of colon adenocarcinomas and colon_n subfolder with 5000 images of benign colonic tissues. The subfolder lung_image_sets contains three secondary subfolders: lung_aca subfolder with 5000 images of lung adenocarcinomas, lung_scc subfolder with 5000 images of lung squamous cell carcinomas, and lung_n subfolder with 5000 images of benign lung tissues. ``` File counts ./lung_image_sets/lung_aca : 5000 ./lung_image_sets/lung_n : 5000 ./lung_image_sets/lung_scc : 5000 ./colon_image_sets/colon_n : 5000 ./colon_image_sets/colon_aca : 5000 ``` https://i.imgur.com/aVdT3ks.jpeg https://i.imgur.com/TNhOxXJ.png }, terms= {Borkowski AA, Bui MM, Thomas LB, Wilson CP, DeLand LA, Mastorides SM. Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv:1912.12142v1 [eess.IV], 2019}, license= {}, superseded= {}, url= {https://github.com/tampapath/lung_colon_image_set/} }

LNDb CT scan dataset (training) (Dataset)

@article{, title= {LNDb CT scan dataset (training)}, keywords= {}, author= {João Pedrosa and Guilherme Aresta and Carlos Ferreira and Márcio Rodrigues and Patrícia Leitão and André Silva Carvalho and João Rebelo and Eduardo Negrão and Isabel Ramos and António Cunha and Aurélio Campilho}, abstract= {The main goal of this challenge is the automatic classification of chest CT scans according to the 2017 Fleischner society pulmonary nodule guidelines for patient follow-up recommendation. The LNDb dataset contains 294 CT scans collected retrospectively at the Centro Hospitalar e Universitário de São João (CHUSJ) in Porto, Portugal between 2016 and 2018. All data was acquired under approval from the CHUSJ Ethical Commitee and was anonymised prior to any analysis to remove personal information except for patient birth year and gender. Further details on patient selection and data acquisition can be consulted on the database description paper. Each CT scan was read by at least one radiologist at CHUSJ to identify pulmonary nodules and other suspicious lesions. A total of 5 radiologists with at least 4 years of experience reading up to 30 CTs per week participated in the annotation process throughout the project. Annotations were performed in a single blinded fashion, i.e. a radiologist would read the scan once and no consensus or review between the radiologists was performed. Each scan was read by at least one radiologist. The instructions for manual annotation were adapted from LIDC-IDRI. Each radiologist identified the following lesions: - nodule ⩾3mm: any lesion considered to be a nodule by the radiologist with greatest in-plane dimension larger or equal to 3mm; - nodule <3mm: any lesion considered to be a nodule by the radiologist with greatest in-plane dimension smaller than 3mm; - non-nodule: any pulmonary lesion considered not to be a nodule by the radiologist, but that contains features which could make it identifiable as a nodule; The annotation process varied for the different categories. Nodules ⩾3mm were segmented and subjectively characterized according to LIDC-IDRI (ratings on subtlety, internal structure, calcification, sphericity, margin, lobulation, spiculation, texture and likelihood of malignancy). For a complete description of these characteristics the reader is referred to McNitt-Gray et al.. For nodules <3mm the nodule centroid was marked and subjective assessment of the nodule's characteristics was performed. For non-nodules, only the lesion centroid was marked. Given that different radiologists may have read the same CT and no consensus review was performed, variability in radiologist annotations is expected. Note that from the 294 CTs of the LNDb dataset, 58 CTs with annotations by at least two radiologists have been withheld for the test set, as well as the corresponding annotations. https://i.imgur.com/MiHSh9c.png}, terms= {The dataset, or any data derived from it, cannot be given or redistributed under any circumstances to persons not belonging to the registered team. If the data in the dataset is remixed, transformed or built upon, the modified data cannot be redistributed under any circumstances; The dataset cannot be used for commercial purposed under any circumstances; Appropriate credit must be given to the authors any time this data is used, independent of purpose. Attribution must be done through citation of the database description paper (https://arxiv.org/abs/1911.08434) or (after publication) to the main challenge publication.}, license= {https://creativecommons.org/licenses/by-nc-nd/4.0/}, superseded= {}, url= {https://lndb.grand-challenge.org/Data/} }

NIH Chest X-ray Dataset (Resized to 224x224) (Dataset)

@article{, title= {NIH Chest X-ray Dataset (Resized to 224x224)}, journal= {}, author= {National Institutes of Health - Clinical Center}, year= {}, url= {https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community}, abstract= {This dataset is resized versions of images to 224x224. ![](https://i.imgur.com/1InHgLs.png) (1, Atelectasis; 2, Cardiomegaly; 3, Effusion; 4, Infiltration; 5, Mass; 6, Nodule; 7, Pneumonia; 8, Pneumothorax; 9, Consolidation; 10, Edema; 11, Emphysema; 12, Fibrosis; 13, Pleural_Thickening; 14 Hernia) ### Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neural networks for thorax disease recognition is severely limited by the availability of only 4143 frontal view images [3] (Openi is the previous largest publicly available chest X-ray dataset to date). In this database, we provide an enhanced version (with 6 more disease categories and more images as well) of the dataset used in the recent work [1] which is approximately 27 times of the number of frontal chest x-ray images in [3]. Our dataset is extracted from the clinical PACS database at National Institutes of Health Clinical Center and consists of ~60% of all frontal chest x-rays in the hospital. Therefore we expect this dataset is significantly more representative to the real patient population distributions and realistic clinical diagnosis challenges, than any previous chest x-ray datasets. Of course, the size of our dataset, in terms of the total numbers of images and thorax disease frequencies, would better facilitate deep neural network training [2]. Refer to [1] on the details of how the dataset is extracted and image labels are mined through natural language processing (NLP). ### Details: ChestX-ray dataset comprises 112,120 frontal-view X-ray images of 30,805 unique patients with the text-mined fourteen disease image labels (where each image can have multi-labels), mined from the associated radiological reports using natural language processing. Fourteen common thoracic pathologies include Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural_thickening, Cardiomegaly, Nodule, Mass and Hernia, which is an extension of the 8 common disease patterns listed in our CVPR2017 paper. Note that original radiology reports (associated with these chest x-ray studies) are not meant to be publicly shared for many reasons. The text-mined disease labels are expected to have accuracy >90%.Please find more details and benchmark performance of trained models based on 14 disease labels in our arxiv paper: https://arxiv.org/abs/1705.02315 ### Contents: 1. 112,120 frontal-view chest X-ray PNG images in 1024*1024 resolution (under images folder) 2. Meta data for all images (Data_Entry_2017.csv): Image Index, Finding Labels, Follow-up #, Patient ID, Patient Age, Patient Gender, View Position, Original Image Size and Original Image Pixel Spacing. 3. Bounding boxes for ~1000 images (BBox_List_2017.csv):Image Index, Finding Label, Bbox[x, y, w, h]. [x y] are coordinates of each box's topleft corner. [w h] represent the width and height of each box. If you find the dataset useful for your research projects, please cite our CVPR 2017 paper:Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, MohammadhadiBagheri, Ronald M. Summers.ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, IEEE CVPR, pp. 3462-3471,2017 ``` @InProceedings{wang2017chestxray,author = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald}, title = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases}, booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)}, pages = {3462--3471}, year = {2017}} ``` ### Questions/Comments: (xiaosong.wang@nih.gov; le.lu@nih.gov; rms@nih.gov) ### Limitations: 1. The image labels are NLP extracted so there would be some erroneous labels but the NLP labelling accuracy is estimated to be >90%. 2. Very limited numbers of disease region bounding boxes. 3. Chest x-ray radiology reports are not anticipated to be publicly shared. Parties who use this public dataset are encouraged to share their “updated” image labels and/or new bounding boxes in their own studied later, maybe through manual annotation. ### Acknowledgement: This work was supported by the Intramural Research Program of the NIH Clinical Center (clinicalcenter.nih.gov) and National Library of Medicine (www.nlm.nih.gov). We thank NVIDIA Corporation for the GPU donations. ### Reference: [1] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, MohammadhadiBagheri, Ronald Summers, ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common ThoraxDiseases, IEEE CVPR, pp. 3462-3471,2017 [2] Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers, Learning to Read Chest X-Rays: Recurrent Neural CascadeModel for Automated Image Annotation, IEEE CVPR, pp. 2497-2506, 2016 [3] Open-i: An open access biomedical search engine. https: //openi.nlm.nih.gov ![](https://www.nih.gov/sites/default/files/styles/featured_media_breakpoint-medium/public/news-events/news-releases/2017/20170927-lung-mass.jpg?itok=wSFXjg6d×tamp=1506520936) }, keywords= {}, terms= {}, license= {"The usage of the data set is unrestricted"}, superseded= {} }

Ocular Disease Intelligent Recognition ODIR-5K (Dataset)

@article{, title= {Ocular Disease Intelligent Recognition ODIR-5K}, keywords= {}, author= {}, abstract= {We collected a structured ophthalmic database of 5,000 patients with age, color fundus photographs from left and right eyes and doctors' diagnostic keywords from doctors (in short, ODIR-5K). This dataset is ‘‘real-life’’ set of patient information collected by Shanggong Medical Technology Co., Ltd. from different hospitals/medical centers in China. In these institutions, fundus images are captured by various cameras in the market, such as Canon, Zeiss and Kowa, resulting into varied image resolutions. Patient identifying information will be removed. Annotations are labeled by trained human readers with quality control management. They classify patient into eight labels including normal (N), diabetes (D), glaucoma (G), cataract (C), AMD (A), hypertension (H), myopia (M) and other diseases/abnormalities (O) based on both eye images and additionally patient age. The publishing of this dataset follows the ethical and privacy rules of China. Table 1 shows one record from ODIR-5K dataset. The 5,000 patients in this challenge are divided into training, off-site testing and on-site testing subsets. Almost 4,000 cases are used in training stage while others are for testing stages (off-site and on-site). Table 2 shows the distribution of case number with respect to eight labels in different stages. Note: one patient may contains one or multiple labels. https://i.imgur.com/vXa8rU9.png https://i.imgur.com/Hs7kYUF.png }, terms= {}, license= {}, superseded= {}, url= {https://odir2019.grand-challenge.org/} }

L1000 Connectivity Map perturbational profiles from Broad Institute LINCS Center for Transcriptomics LINCS PHASE II (n=354,123; updated March 30, 2017) (Level 5 data) (Dataset)

@article{, title= {L1000 Connectivity Map perturbational profiles from Broad Institute LINCS Center for Transcriptomics LINCS PHASE *II* (n=354,123; updated March 30, 2017) (Level 5 data)}, keywords= {}, author= {LINCS}, abstract= {The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gene-expression changes. This is Level 5 data: GSE70138_Broad_LINCS_Level5_COMPZ_n118050x12328_2017-03-06.gctx.gz Series GSE70138 L1000 data is provided at five levels of the data processing pipeline: Level 1: Raw unprocessed flow cytometry data from Luminex (LXB) Level 2: Gene expression values per 1000 genes after deconvolution (GEX) Level 3: Quantile-normalized gene expression profiles of landmark genes and imputed transcripts (Q2NORM or INF) Level 4: Gene signatures computed using z-scores relative to the plate population as control (ZSPCINF) or relative to the plate vehicle control (ZSVCINF) Level 5: Differential gene expression signatures https://i.imgur.com/zIeOFMt.png }, terms= {}, license= {}, superseded= {}, url= {https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70138}, year= {2017} }

1000 Fundus images with 39 categories (Dataset)

@article{, title= {1000 Fundus images with 39 categories}, keywords= {}, author= {Joint Shantou International Eye Centre (JSIEC)}, abstract= {All these 1000 fundus images which belong to 39 classes are come from the Joint Shantou International Eye Centre (JSIEC), Shantou city, Guangdong province ,China. These images are a small part of total 209,494 fundus images to be used for training validating and testing our deep learning platform. The copyright of these images belongs to JSIEC, and can be freely used for any purpose. https://i.imgur.com/kWGUlMo.jpg ``` 3.1M1000images/12.Disc swelling and elevation 2.6M1000images/26.Fibrosis 23M1000images/0.0.Normal 2.4M1000images/15.1.Bietti crystalline dystrophy 3.8M1000images/22.Cotton-wool spots 9.8M1000images/8.MH 4.5M1000images/24.Chorioretinal atrophy-coloboma 3.4M1000images/25.Preretinal hemorrhage 2.9M1000images/14.Congenital disc abnormality 4.7M1000images/28.Silicon oil in eye 19M1000images/1.1.DR3 3.5M1000images/16.Peripheral retinal degeneration and break 4.4M1000images/10.0.Possible glaucoma 28M1000images/1.0.DR2 19M1000images/0.2.Large optic cup 8.9M1000images/7.ERM 22M1000images/9.Pathological myopia 2.8M1000images/20.Massive hard exudates 74M1000images/29.0.Blur fundus without PDR 12M1000images/15.0.Retinitis pigmentosa 2.8M1000images/13.Dragged Disc 5.6M1000images/5.1.VKH disease 27M1000images/4.Rhegmatogenous RD 33M1000images/6.Maculopathy 12M1000images/0.3.DR1 12M1000images/21.Yellow-white spots-flecks 19M1000images/2.0.BRVO 1.6M1000images/19.Fundus neoplasm 13M1000images/29.1.Blur fundus with suspected PDR 4.6M1000images/2.1.CRVO 5.0M1000images/23.Vessel tortuosity 5.5M1000images/10.1.Optic atrophy 6.2M1000images/5.0.CSCR 4.3M1000images/11.Severe hypertensive retinopathy 2.8M1000images/17.Myelinated nerve fiber 6.1M1000images/0.1.Tessellated fundus 5.3M1000images/27.Laser Spots 3.7M1000images/18.Vitreous particles 3.6M1000images/3.RAO 429M1000images ```}, terms= {}, license= {can be freely used for any purpose}, superseded= {}, url= {https://www.kaggle.com/linchundan/fundusimage1000} }

MRI Dataset for Hippocampus Segmentation (HFH) (hippseg_2011) (Dataset)

@article{, title= {MRI Dataset for Hippocampus Segmentation (HFH) (hippseg_2011)}, keywords= {}, author= {K. Jafari-Khouzani and K. Elisevich, S. Patel and H. Soltanian-Zadeh}, abstract= {This dataset contains T1-weighted MR images of 50 subjects, 40 of whom are patients with temporal lobe epilepsy and 10 are nonepileptic subjects. Hippocampus labels are provided for 25 subjects for training. The users may submit their segmentation outcomes for the remaining 25 testing images to get a table of segmentation metrics. https://i.imgur.com/XSJr6oQ.png https://i.imgur.com/jWpnVeu.gif ``` HFH ├── ReadMe.txt ├── Test │ ├── HFH_026.hdr │ ├── HFH_026.img │ ├── HFH_027.hdr │ ├── HFH_027.img │ ├── HFH_028.hdr │ ├── HFH_028.img │ ├── HFH_029.hdr │ ├── HFH_029.img │ ├── HFH_030.hdr │ ├── HFH_030.img │ ├── HFH_031.hdr │ ├── HFH_031.img │ ├── HFH_032.hdr │ ├── HFH_032.img │ ├── HFH_033.hdr │ ├── HFH_033.img │ ├── HFH_034.hdr │ ├── HFH_034.img │ ├── HFH_035.hdr │ ├── HFH_035.img │ ├── HFH_036.hdr │ ├── HFH_036.img │ ├── HFH_037.hdr │ ├── HFH_037.img │ ├── HFH_038.hdr │ ├── HFH_038.img │ ├── HFH_039.hdr │ ├── HFH_039.img │ ├── HFH_040.hdr │ ├── HFH_040.img │ ├── HFH_041.hdr │ ├── HFH_041.img │ ├── HFH_042.hdr │ ├── HFH_042.img │ ├── HFH_043.hdr │ ├── HFH_043.img │ ├── HFH_044.hdr │ ├── HFH_044.img │ ├── HFH_045.hdr │ ├── HFH_045.img │ ├── HFH_046.hdr │ ├── HFH_046.img │ ├── HFH_047.hdr │ ├── HFH_047.img │ ├── HFH_048.hdr │ ├── HFH_048.img │ ├── HFH_049.hdr │ ├── HFH_049.img │ ├── HFH_050.hdr │ └── HFH_050.img └── Train ├── HFH_001.hdr ├── HFH_001.img ├── HFH_002.hdr ├── HFH_002.img ├── HFH_003.hdr ├── HFH_003.img ├── HFH_004.hdr ├── HFH_004.img ├── HFH_005.hdr ├── HFH_005.img ├── HFH_006.hdr ├── HFH_006.img ├── HFH_007.hdr ├── HFH_007.img ├── HFH_008.hdr ├── HFH_008.img ├── HFH_009.hdr ├── HFH_009.img ├── HFH_010.hdr ├── HFH_010.img ├── HFH_011.hdr ├── HFH_011.img ├── HFH_012.hdr ├── HFH_012.img ├── HFH_013.hdr ├── HFH_013.img ├── HFH_014.hdr ├── HFH_014.img ├── HFH_015.hdr ├── HFH_015.img ├── HFH_016.hdr ├── HFH_016.img ├── HFH_017.hdr ├── HFH_017.img ├── HFH_018.hdr ├── HFH_018.img ├── HFH_019.hdr ├── HFH_019.img ├── HFH_020.hdr ├── HFH_020.img ├── HFH_021.hdr ├── HFH_021.img ├── HFH_022.hdr ├── HFH_022.img ├── HFH_023.hdr ├── HFH_023.img ├── HFH_024.hdr ├── HFH_024.img ├── HFH_025.hdr ├── HFH_025.img └── Labels ├── HFH_001_Hipp_Labels.hdr ├── HFH_001_Hipp_Labels.img ├── HFH_002_Hipp_Labels.hdr ├── HFH_002_Hipp_Labels.img ├── HFH_003_Hipp_Labels.hdr ├── HFH_003_Hipp_Labels.img ├── HFH_004_Hipp_Labels.hdr ├── HFH_004_Hipp_Labels.img ├── HFH_005_Hipp_Labels.hdr ├── HFH_005_Hipp_Labels.img ├── HFH_006_Hipp_Labels.hdr ├── HFH_006_Hipp_Labels.img ├── HFH_007_Hipp_Labels.hdr ├── HFH_007_Hipp_Labels.img ├── HFH_008_Hipp_Labels.hdr ├── HFH_008_Hipp_Labels.img ├── HFH_009_Hipp_Labels.hdr ├── HFH_009_Hipp_Labels.img ├── HFH_010_Hipp_Labels.hdr ├── HFH_010_Hipp_Labels.img ├── HFH_011_Hipp_Labels.hdr ├── HFH_011_Hipp_Labels.img ├── HFH_012_Hipp_Labels.hdr ├── HFH_012_Hipp_Labels.img ├── HFH_013_Hipp_Labels.hdr ├── HFH_013_Hipp_Labels.img ├── HFH_014_Hipp_Labels.hdr ├── HFH_014_Hipp_Labels.img ├── HFH_015_Hipp_Labels.hdr ├── HFH_015_Hipp_Labels.img ├── HFH_016_Hipp_Labels.hdr ├── HFH_016_Hipp_Labels.img ├── HFH_017_Hipp_Labels.hdr ├── HFH_017_Hipp_Labels.img ├── HFH_018_Hipp_Labels.hdr ├── HFH_018_Hipp_Labels.img ├── HFH_019_Hipp_Labels.hdr ├── HFH_019_Hipp_Labels.img ├── HFH_020_Hipp_Labels.hdr ├── HFH_020_Hipp_Labels.img ├── HFH_021_Hipp_Labels.hdr ├── HFH_021_Hipp_Labels.img ├── HFH_022_Hipp_Labels.hdr ├── HFH_022_Hipp_Labels.img ├── HFH_023_Hipp_Labels.hdr ├── HFH_023_Hipp_Labels.img ├── HFH_024_Hipp_Labels.hdr ├── HFH_024_Hipp_Labels.img ├── HFH_025_Hipp_Labels.hdr └── HFH_025_Hipp_Labels.img 3 directories, 151 files ```}, terms= {The dataset is free to use for research and education. Please refer to the following article if you use it in your publications: K. Jafari-Khouzani, K. Elisevich, S. Patel, and H. Soltanian-Zadeh, “Dataset of magnetic resonance images of nonepileptic subjects and temporal lobe epilepsy patients for validation of hippocampal segmentation techniques,” Neuroinformatics, 2011.}, license= {free to use for research and education}, superseded= {}, url= {https://www.nitrc.org/projects/hippseg_2011/} }

Longitudinal diabetic retinopathy screening data (Dataset)

@article{, title= {Longitudinal diabetic retinopathy screening data}, keywords= {}, author= {}, abstract= {This data set contains repeated 4-field color fundus photos (1120 in total) of 70 patients in the diabetic retinopathy screening program of the Rotterdam Eye Hospital (Rotterdam, The Netherlands); the result of intra- and inter-visit registration by two methods (i2kRetina and WeVaR); and the grading and ranking of these results by two graders. ### Inclusion criteria: All patients with diabetes who were screened for diabetic retinopathy during one week in June 2013. ### Exclusion criteria: First-time patients and Patients who were not examined in the year before. Fundus image of both eyes of each patient were acquired using a non-mydriatic digital fundus camera (Topcon TRC-NW65) with a 45 degrees field of view after pupil dilation. Images of each visit were registered by two methods: WeVaR (see Adal et al, A Hierarchical Coarse-to-Fine Approach for Image Registration) and i2k Retina (DualAlign LLC; see here). Image mosaic movies were created from the registered, normalized fundus images and were presented to two graders. In addition, the graders compared the image mosaics side-by-side and ranked them. Images of consecutive visits were similarly registered and the image mosaic movies were again presented to two graders. Included data: Patient's gender and age All color fundus images All normalized fundus images Intra-visit image mosaic movies for both registration methods Intra-visit Image mosaics for both registration methods Inter-visit image mosaic movies for both registration methods Scores of two graders (not every movie/image was graded by both graders) ### Publications This data set, or a part thereof, was used by us in the following paper(s). Please cite one or more of these papers in any of your publication(s) that uses (parts of) this data set. K.M. Adal, P.G. van Etten, J.P. Martinez, L.J. van Vliet, K.A. Vermeer. Accuracy Assessment of Intra and Inter-Visit Fundus Image Registration for Diabetic Retinopathy Screening. Invest Ophthalmol Vis Sci. 2015. Accepted for publication. ### Contributors The following people did all the hard work on assembling and releasing this data set: Kedir Adal, Peter van Etten, Jose Martinez and Koen Vermeer https://i.imgur.com/suusenq.png }, terms= {The Rotterdam Ophthalmic Institute allows you to use this data free of charges, provided that you do not use the data for commercial activities; you do not hold us liable for any errors in the data set; you do not redistribute the data; any incidental findings will be reported back to us; and you will cite one or more of our relevant publications in all publications that make use of this data.}, license= {}, superseded= {}, url= {http://www.rodrep.com/longitudinal-diabetic-retinopathy-screening---description.html} }

RIGA dataset (Retinal fundus images for glaucoma analysis) (Dataset)

@article{, title= {RIGA dataset (Retinal fundus images for glaucoma analysis)}, keywords= {}, author= {Ahmed Almazroa, Sami Alodhayb, Essameldin Osman, Eslam Ramadan, Mohammed Hummadi, Mohammed Dlaim, Muhannad Alkatee, Kaamran Raahemifar, Vasudevan Lakshminarayanan}, abstract= {A de-identified dataset of retinal fundus images for glaucoma analysis (RIGA) was derived from three sources. The optic cup and disc boundaries of these images were marked and annotated manually by six experienced ophthalmologists individually using a tablet and a precise pen. Six parameters were extracted and assessed among the ophthalmologists. The inter-observer annotations were compared by calculating the standard deviation (SD) for every image between the six ophthalmologists in order to determine if there are any outliers among the six annotations to be eliminated i.e. filtering the images. The dataset includes 3 different files: 1) MESSIDOR dataset file contains 460 original images and 460 images for every single ophthalmologist manual marking in total of 3220 images for the entire file. 2) Bin Rushed Ophthalmic center file and contains 195 original images and 195 images for every sin... [more] Ahmed Almazroa, Sami Alodhayb, Essameldin Osman, Eslam Ramadan, Mohammed Hummadi, Mohammed Dlaim, Muhannad Alkatee, Kaamran Raahemifar, Vasudevan Lakshminarayanan, "Retinal fundus images for glaucoma analysis: the RIGA dataset", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790B (6 March 2018); doi: 10.1117/12.2293584; https://doi.org/10.1117/12.2293584 https://i.imgur.com/5y3h0Vr.png }, terms= {}, license= {http://creativecommons.org/licenses/by-nc/4.0/}, superseded= {}, url= {https://deepblue.lib.umich.edu/data/concern/data_sets/3b591905z?locale=en } }

P. vivax (malaria) infected human blood smears (BBBC041) (Dataset)

@article{, title= {P. vivax (malaria) infected human blood smears (BBBC041)}, keywords= {}, author= {}, abstract= {### Description of the biological application Malaria is a disease caused by Plasmodium parasites that remains a major threat in global health, affecting 200 million people and causing 400,000 deaths a year. The main species of malaria that affect humans are Plasmodium falciparum and Plasmodium vivax. For malaria as well as other microbial infections, manual inspection of thick and thin blood smears by trained microscopists remains the gold standard for parasite detection and stage determination because of its low reagent and instrument cost and high flexibility. Despite manual inspection being extremely low throughput and susceptible to human bias, automatic counting software remains largely unused because of the wide range of variations in brightfield microscopy images. However, a robust automatic counting and cell classification solution would provide enormous benefits due to faster and more accurate quantitative results without human variability; researchers and medical professionals could better characterize stage-specific drug targets and better quantify patient reactions to drugs. Previous attempts to automate the process of identifying and quantifying malaria have not gained major traction partly due to difficulty of replication, comparison, and extension. Authors also rarely make their image sets available, which precludes replication of results and assessment of potential improvements. The lack of a standard set of images nor standard set of metrics used to report results has impeded the field. ### Images Images are in .png or .jpg format. There are 3 sets of images consisting of 1364 images (~80,000 cells) with different researchers having prepared each one: from Brazil (Stefanie Lopes), from Southeast Asia (Benoit Malleret), and time course (Gabriel Rangel). Blood smears were stained with Giemsa reagent. ### Ground truth The data consists of two classes of uninfected cells (RBCs and leukocytes) and four classes of infected cells (gametocytes, rings, trophozoites, and schizonts). Annotators were permitted to mark some cells as difficult if not clearly in one of the cell classes. The data had a heavy imbalance towards uninfected RBCs versus uninfected leukocytes and infected cells, making up over 95% of all cells. A class label and set of bounding box coordinates were given for each cell. For all data sets, infected cells were given a class label by Stefanie Lopes, malaria researcher at the Dr. Heitor Vieira Dourado Tropical Medicine Foundation hospital, indicating stage of development or marked as difficult. ### For more information These images were contributed by Jane Hung of MIT and the Broad Institute in Cambridge, MA. https://i.imgur.com/1zrfx2Y.png}, terms= {}, license= {Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License }, superseded= {}, url= {https://data.broadinstitute.org/bbbc/BBBC041/} }

Images of thin blood smears with bounding boxes around malaria parasites (malaria-655) (Dataset)

@article{, title= {Images of thin blood smears with bounding boxes around malaria parasites (malaria-655)}, keywords= {}, author= {F Boray Tek and Andrew G Dempster and Izzet Kale}, abstract= {655 images of thin smears with bounding boxes around parasites Tek FB, Dempster AG, Kale I, Parasite detection and identification for automated thin blood film malaria diagnosis. Computer Vision and Image Understanding 2010, 114:21-32. https://i.imgur.com/E30zLVQ.png}, terms= {}, license= {}, superseded= {}, url= {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719653/} }

ISIC2017: Skin Lesion Analysis Towards Melanoma Detection (Dataset)

@article{, title= {ISIC2017: Skin Lesion Analysis Towards Melanoma Detection}, keywords= {}, author= {Codella N and Gutman D and Celebi ME and Helba B and Marchetti MA and Dusza S and Kalloo A and Liopyris K and Mishra N and Kittler H and Halpern A}, abstract= {The goal of the challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images. Image analysis of skin lesions is composed of 3 parts: - Part 1: Lesion Segmentation - Part 2: Detection and Localization of Visual Dermoscopic Features/Patterns - Part 3: Disease Classification This challenge provides training data (~2000 images) for participants to engage in all 3 components of lesion image analysis. A separate public validation dataset (~150 images) and blind held-out test dataset (~600 images) will be provided for participants to generate and submit automated results. ## Background ### Melanoma Skin cancer is a major public health problem, with over 5 million newly diagnosed cases in the United States each year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths each year. ### Dermoscopy As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by expert visual inspection. It is also amenable to automated detection with image analysis. Given the widespread availability of high-resolution cameras, algorithms that can improve our ability to screen and detect troublesome lesions can be of great value. As a result, many centers have begun their own research efforts on automated analysis. However, a centralized, coordinated, and comparative effort across institutions has yet to be implemented. Dermoscopy is an imaging technique that eliminates the surface reflection of skin. By removing surface reflection, visualization of deeper levels of skin is enhanced. Prior research has shown that when used by expert dermatologists, dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. As inexpensive consumer dermatoscope attachments for smart phones are beginning to reach the market, the opportunity for automated dermoscopic assessment algorithms to positively influence patient care increases. https://i.imgur.com/daTTwFV.png ## Citation: Codella N, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza S, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A. "Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)". arXiv: 1710.05006 [cs.CV] Available: https://arxiv.org/abs/1710.05006 }, terms= {}, license= {}, superseded= {}, url= {} }

ISIC2018: Skin Lesion Analysis Towards Melanoma Detection (Dataset)

@article{, title= {ISIC2018: Skin Lesion Analysis Towards Melanoma Detection}, keywords= {}, author= {Noel Codella and Veronica Rotemberg and Philipp Tschandl and M. Emre Celebi and Stephen Dusza and David Gutman and Brian Helba and Aadi Kalloo and Konstantinos Liopyris and Michael Marchetti and Harald Kittler and Allan Halpern}, abstract= {This challenge is broken into three separate tasks: - Task 1: Lesion Segmentation - Task 2: Lesion Attribute Detection - Task 3: Disease Classification https://i.imgur.com/daTTwFV.png When using the ISIC 2018 datasets in your research, please cite the following works: [1] Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, Allan Halpern: “Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)”, 2018; https://arxiv.org/abs/1902.03368 [2] Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi:10.1038/sdata.2018.161 (2018).}, terms= {}, license= {Creative Commons Attribution-NonCommercial 4.0 International Public License}, superseded= {}, url= {https://challenge2018.isic-archive.com/} }

2D ultrasound sequences of the liver (mp4) (Dataset)

@article{, title= {2D ultrasound sequences of the liver (mp4)}, keywords= {}, author= {}, abstract= {7 2D ultrasound sequences of the liver of healthy volunteers were acquired during free breathing over a period of 5-10 min. This is a converted version of the usliverseq dataset into mp4 files encoded with H.264 to reduce size and make the files easier to read. The conversion commands used are: ``` $vlc volunteer06.avi --video-filter=scene --scene-prefix=movie --scene-ratio=1 --scene-path=volunteer06 --no-skip-frames $ffmpeg -r 17 -i volunteer06/movie%05d.png volunteer06.mp4 $ffmpeg -r 25 -start_number 580 -i volunteer01/Image_120910_102034_%05d.bmp volunteer01.mp4 ``` ### Data notes: ``` Ultrasound device: Antares, Siemens Medical Solutions, Mountain View, CA, USA Ultrasound transducer: CH4-1 Place of acquisition: Radiology Department of Geneva University Hospital, Switzerland volunteer02.avi - volunteer 09.avi: 8 bits grayscale codec volunteer01: sequence of images grabbed from the ultraosund video output Spatial resolutionTemporal resolution Center frequency [mm/pixel] [fps] [MHz] ----------------------------------------------------------------------- volunteer010.7125 2.22 ----------------------------------------------------------------------- volunteer020.4016 2.00 ----------------------------------------------------------------------- volunteer030.3617 1.82 ----------------------------------------------------------------------- volunteer040.4215 2.22 ----------------------------------------------------------------------- volunteer050.4015 2.22 ----------------------------------------------------------------------- volunteer060.3717 1.82 ----------------------------------------------------------------------- volunteer070.2814 2.22 ----------------------------------------------------------------------- volunteer080.3617 1.82 ----------------------------------------------------------------------- volunteer09 0.4016 1.82 ``` ### Example images |01 | 02 | 03 | 04 | 05| 06 | 07| |---|---|---|--|--|--| |https://i.imgur.com/0E5C348.png | https://i.imgur.com/1foqqvE.png | https://i.imgur.com/Zm66UCu.png |https://i.imgur.com/qq7K2eT.png | https://i.imgur.com/Ulw0yTv.png | https://i.imgur.com/TVauYUt.png | https://i.imgur.com/ffbeBHP.png | ## Citation L. Petrusca, P. Cattin, V. De Luca, F. Preiswerk, Z. Celicanin, V. Auboiroux, M. Viallon, P. Arnold, F. Santini, S. Terraz, K. Scheffler, C. D. Becker, R. Salomir, "Hybrid Ultrasound/Magnetic Resonance Simultaneous Acquisition and Image Fusion for Motion Monitoring in the Upper Abdomen", Investigative Radiology, Vol. 48, No. 5, pp. 333-340, 2013. V. De Luca, M. Tschannen, G. SzÃ©kely, C. Tanner, "A Learning-based Approach for Fast and Robust Vessel Tracking in Long Ultrasound Sequences", Medical Image Computing and Computer-Assisted Intervention, Springer. volume of LNCS 8149, pp. 518-525, 2013. }, terms= {}, license= {}, superseded= {}, url= {http://www.vision.ee.ethz.ch/en/datasets/} }

DRIMDB (Diabetic Retinopathy Images Database) Database for Quality Testing of Retinal Images (Dataset)

@article{, title= {DRIMDB (Diabetic Retinopathy Images Database) Database for Quality Testing of Retinal Images}, keywords= {fundus}, author= {}, abstract= {Retinal image quality assessment (IQA) is a crucial process for automated retinal image analysis systems to obtain an accurate and successful diagnosis of retinal diseases. Consequently, the first step in a good retinal image analysis system is measuring the quality of the input image. We present an approach for finding medically suitable retinal images for retinal diagnosis. We used a three-class grading system that consists of good, bad, and outlier classes. We created a retinal image quality dataset with a total of 216 consecutive images called the Diabetic Retinopathy Image Database. We identified the suitable images within the good images for automatic retinal image analysis systems using a novel method. Subsequently, we evaluated our retinal image suitability approach using the Digital Retinal Images for Vessel Extraction and Standard Diabetic Retinopathy Database Calibration level 1 public datasets. The results were measured through the F1 metric, which is a harmonic mean of precision and recall metrics. The highest F1 scores of the IQA tests were 99.60%, 96.50%, and 85.00% for good, bad, and outlier classes, respectively. Additionally, the accuracy of our suitable image detection approach was 98.08%. Our approach can be integrated into any automatic retinal analysis system with sufficient performance scores. Good: https://i.imgur.com/D5unNKs.png Bad: https://i.imgur.com/slFzaCZ.png Outlier: https://i.imgur.com/eG4PDet.png}, terms= {}, license= {}, superseded= {}, url= {https://pubmed.ncbi.nlm.nih.gov/24718384/} }

DiaRetDB1 V2.1 - Diabetic Retinopathy Database (Dataset)

@article{, title= {DiaRetDB1 V2.1 - Diabetic Retinopathy Database}, keywords= {}, author= {Machine Vision and Pattern Recognition Laboratory}, abstract= {The DiaRetDB1 is a public database for evaluating and benchmarking diabetic retinopathy detection algorithms. The database contains digital images of eye fundus and expert annotated ground truth for several well-known diabetic fundus lesions (hard exudates, soft exudates, microaneurysms and hemorrhages). The original images and the raw ground truth are both available. In addition to the data we also provide Matlab functionality (M-files) to read data (XML-files), fuse data of several experts and to evaluate detection methods. This database is related to ImageRet project and the ground truth was collected using our ImgAnnoTool image annotation tool (contact Lasse Lensu for more information). For a more detailed description, see our documentation, please. ### Authors The following authors have significantly contributed to the actual work of establishing and collecting the data and implementing the methods for the database: Tomi Kauppi, Valentina Kalesnykiene, Iiris Sorri, Asta Raninen, Raija Voutilainen, Joni Kamarainen, Lasse Lensu and Hannu Uusitalo. 90 images https://i.imgur.com/Oy7GJSR.png }, terms= {}, license= {}, superseded= {}, url= {http://www.it.lut.fi/project/imageret/diaretdb1_v2_1/} }

MIT-BIH Arrhythmia Database (Dataset)

@article{, title= {MIT-BIH Arrhythmia Database}, keywords= {}, author= {Moody GB, Mark RG.}, abstract= {Since 1975, our laboratories at Boston's Beth Israel Hospital (now the Beth Israel Deaconess Medical Center) and at MIT have supported our own research into arrhythmia analysis and related subjects. One of the first major products of that effort was the MIT-BIH Arrhythmia Database, which we completed and began distributing in 1980. The database was the first generally available set of standard test material for evaluation of arrhythmia detectors, and has been used for that purpose as well as for basic research into cardiac dynamics at more than 500 sites worldwide. Originally, we distributed the database on 9-track half-inch digital tape at 800 and 1600 bpi, and on quarter-inch IRIG-format FM analog tape. In August, 1989, we produced a CD-ROM version of the database. The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample. The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included with the database. This directory contains the entire MIT-BIH Arrhythmia Database. About half (25 of 48 complete records, and reference annotation files for all 48 records) of this database has been freely available here since PhysioNet's inception in September 1999. The 23 remaining signal files, which had been available only on the MIT-BIH Arrhythmia Database CD-ROM, were posted here in February 2005. Much more information about this database may be found in the MIT-BIH Arrhythmia Database Directory. ## Citation Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209)}, terms= {}, license= {}, superseded= {}, url= {https://physionet.org/physiobank/database/mitdb/} }

1000 Genomes Project (Dataset)

@article{, title= {1000 Genomes Project}, journal= {}, author= {1000 Genomes Project}, year= {}, url= {https://www.nature.com/articles/nmeth.1974}, abstract= {Variant count files storing genetic variation across 1092 complete human genomes}, keywords= {}, terms= {}, license= {BY-NC-SA}, superseded= {} }

PROSTATEx (Dataset)

@article{, title= {PROSTATEx}, journal= {}, author= {Geert Litjens and Oscar Debats and Jelle Barentsz and Nico Karssemeijer, and Henkjan Huisman}, year= {}, url= {https://wiki.cancerimagingarchive.net/display/Public/SPIE-AAPM-NCI+PROSTATEx+Challenges}, abstract= {This collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800), and subsequently, the ADC map was calculated by the scanner software. All images were acquired without an endorectal coil. https://i.imgur.com/dh121Ur.png ## Citation G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer and H. Huisman. "Computer-aided detection of prostate cancer in MRI", IEEE Transactions on Medical Imaging 2014;33:1083-1092.}, keywords= {}, terms= {}, license= {}, superseded= {} }

Head-Neck-CT (Dataset)

@article{, title= {Head-Neck-CT}, keywords= {}, author= {}, abstract= {https://i.imgur.com/4jYnRqK.png This is a subset of just the CT scans from the original dataset. "This collection contains FDG-PET/CT and radiotherapy planning CT imaging data of 298 patients from four different institutions in Québec with histologically proven head-and-neck cancer (H&N) All patients had pre-treatment FDG-PET/CT scans between April 2006 and November 2014, and within a median of 18 days (range: 6-66) before treatment Dates in the TCIA images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans." These patients were all part of a study described in further detail (treatment, image scanning protocols, etc.) in the publication: ## Publication Citation Vallières, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7, 10117 (2017). doi: 10.1038/s41598-017-10371-5}, terms= {}, license= {Creative Commons Attribution 3.0 Unported License}, superseded= {}, url= {https://wiki.cancerimagingarchive.net/display/Public/Head-Neck-PET-CT} }

PADCHEST_SJ (Feb 2019 Update) (Dataset)

@article{, title= {PADCHEST_SJ (Feb 2019 Update)}, keywords= {chest xray, radiology}, author= {}, abstract= {This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at Hospital San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. https://i.imgur.com/MpVlYgB.png}, terms= {}, license= {Creative Commons Attribution-ShareAlike 4.0 International License}, superseded= {}, url= {https://arxiv.org/abs/1901.07441} }

Lung CT Segmentation Challenge 2017 (LCTSC) (Dataset)

@article{, title= {Lung CT Segmentation Challenge 2017 (LCTSC)}, keywords= {}, author= {}, abstract= {Average 4DCT or free-breathing (FB) CT images from 60 patients, depending on clinical practice, are used for this challenge. Data were acquired from 3 institutions (20 each). Datasets were divided into three groups, stratified per institution: 36 training datasets 12 off-site test datasets 12 live test datasets https://i.imgur.com/CzjcFRj.png |Collection Statistics| | |--- |--- | |Image Size (GB)|4.8| |Modalities|CT, RT| |Number of Images|9569| |Number of Patients|60| |Number of Series|96| |Number of Studies|60| }, terms= {}, license= {Creative Commons Attribution 3.0 Unported License}, superseded= {}, url= {https://wiki.cancerimagingarchive.net/display/Public/Lung+CT+Segmentation+Challenge+2017} }

Invasive Ductal Carcinoma (IDC) Histology Image Dataset (Dataset)

@article{, title= {Invasive Ductal Carcinoma (IDC) Histology Image Dataset}, keywords= {}, author= {}, abstract= {Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. To assign an aggressiveness grade to a whole mount sample, pathologists typically focus on the regions which contain the IDC. As a result, one of the common pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide. Dataset Description The original dataset consisted of 162 whole mount slide images of Breast Cancer (BCa) specimens scanned at 40x. From that, 277,524 patches of size 50 x 50 were extracted (198,738 IDC negative and 78,786 IDC positive). Each patch’s file name is of the format: ``` u_xX_yY_classC.png — > example 10253_idx5_x1351_y1101_class0.png ``` Where u is the patient ID (10253_idx5), X is the x-coordinate of where this patch was cropped from, Y is the y-coordinate of where this patch was cropped from, and C indicates the class where 0 is non-IDC and 1 is IDC. https://i.imgur.com/sDQrEp2.png }, terms= {}, license= {}, superseded= {}, url= {http://www.andrewjanowczyk.com/use-case-6-invasive-ductal-carcinoma-idc-segmentation/} }

Shenzhen Hospital X-ray Set (Dataset)

@article{, title= {Shenzhen Hospital X-ray Set}, keywords= {radiology}, author= {}, abstract= {X-ray images in this data set have been collected by Shenzhen No.3 Hospital in Shenzhen, Guangdong providence, China. The x-rays were acquired as part of the routine care at Shenzhen Hospital. The set contains images in JPEG format. There are 340 normal x-rays and 275 abnormal x-rays showing various manifestations of tuberculosis. Examples images: https://i.imgur.com/e3fPm3a.png Example clinical reports: ``` male 24yrs PTB in the left lower field ``` ``` female 32yrs Bilateral secondary PTB , right pleural change after decortication ``` ``` female 16yrs PTB in the left upper field ``` ## Citation Candemir S, Jaeger S, Musco J, Xue Z, Karargyris A, Antani SK, Thoma GR, Palaniappan K. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990 Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan FM, Xue Z, Palaniappan K, Singh RK, Antani SK. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. PMID: 24108713}, terms= {}, license= {}, superseded= {}, url= {https://ceb.nlm.nih.gov/repositories/tuberculosis-chest-x-ray-image-data-sets/} }

Montgomery County X-ray Set (Dataset)

@article{, title= {Montgomery County X-ray Set}, keywords= {radiology}, author= {}, abstract= {X-ray images in this data set have been acquired from the tuberculosis control program of the Department of Health and Human Services of Montgomery County, MD, USA. This set contains 138 posterior-anterior x-rays, of which 80 x-rays are normal and 58 x-rays are abnormal with manifestations of tuberculosis. All images are de-identified and available in DICOM format. The set covers a wide range of abnormalities, including effusions and miliary patterns. The data set includes radiology readings available as a text file. https://i.imgur.com/JhQU2S5.png Left Lung (Right lung segmentation also exists) https://i.imgur.com/72dmGdB.png ## Citation Candemir S, Jaeger S, Musco J, Xue Z, Karargyris A, Antani SK, Thoma GR, Palaniappan K. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging. 2014 Feb;33(2):577-90. doi: 10.1109/TMI.2013.2290491. PMID: 24239990 Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan FM, Xue Z, Palaniappan K, Singh RK, Antani SK. Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging. 2014 Feb;33(2):233-45. doi: 10.1109/TMI.2013.2284099. PMID: 24108713}, terms= {}, license= {}, superseded= {}, url= {https://ceb.nlm.nih.gov/repositories/tuberculosis-chest-x-ray-image-data-sets/} }

IDRiD (Indian Diabetic Retinopathy Image Dataset) (Dataset)

@article{, title= {IDRiD (Indian Diabetic Retinopathy Image Dataset)}, keywords= {}, author= {}, abstract= {IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. Moreover, it is the only dataset constituting typical diabetic retinopathy lesions and also normal retinal structures annotated at a pixel level. This dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy. This dataset was available as a part of "Diabetic Retinopathy: Segmentation and Grading Challenge" organised in conjuction with IEEE International Symposium on Biomedical Imaging (ISBI-2018), Washington D.C. The dataset is divided into three parts: A. Segmentation: It consists of 1. Original color fundus images (81 images divided into train and test set - JPG Files) 2. Groundtruth images for the Lesions (Microaneurysms, Haemorrhages, Hard Exudates and Soft Exudates divided into train and test set - TIF Files) and Optic Disc (divided into train and test set - TIF Files) B. Disease Grading: it consists of 1. Original color fundus images (516 images divided into train set (413 images) and test set (103 images) - JPG Files) 2. Groundtruth Labels for Diabetic Retinopathy and Diabetic Macular Edema Severity Grade (Divided into train and test set - CSV File) C. Localization: It consists of 1. Original color fundus images (516 images divided into train set (413 images) and test set (103 images) - JPG Files) 2. Groundtruth Labels for Optic Disc Center Location (Divided into train and test set - CSV File) 3. Groundtruth Labels for Fovea Center Location (Divided into train and test set - CSV File) For more information visit idrid.grand-challenge.org Sample images (scaled down) https://i.imgur.com/gajYxoR.png Sample segmentations of microaneurysms (scaled down) https://i.imgur.com/f8irOmW.png Paper: https://res.mdpi.com/data/data-03-00025/article_deploy/data-03-00025.pdf?filename=&attachment=1 }, terms= {}, license= {Creative Commons Attribution}, superseded= {}, url= {https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid} }

Kaggle Diabetic Retinopathy Detection Training Dataset (DRD) (Dataset)

@article{, title= {Kaggle Diabetic Retinopathy Detection Training Dataset (DRD)}, keywords= {fundus}, author= {}, abstract= {This dataset is a large set of high-resolution retina images taken under a variety of imaging conditions. A left and right field is provided for every subject. Images are labeled with a subject id as well as either left or right (e.g. 1_left.jpeg is the left eye of patient id 1). A clinician has rated the presence of diabetic retinopathy in each image on a scale of 0 to 4, according to the following scale: ``` 0 - No DR 1 - Mild 2 - Moderate 3 - Severe 4 - Proliferative DR ``` Total Images: 35126. The distribution of labels is: {0: 25810, 1: 2443, 2: 5292, 4: 708, 3: 873} Your task is to create an automated analysis system capable of assigning a score based on this scale. The images in the dataset come from different models and types of cameras, which can affect the visual appearance of left vs. right. Some images are shown as one would see the retina anatomically (macula on the left, optic nerve on the right for the right eye). Others are shown as one would see through a microscope condensing lens (i.e. inverted, as one sees in a typical live eye exam). There are generally two ways to tell if an image is inverted: It is inverted if the macula (the small dark central area) is slightly higher than the midline through the optic nerve. If the macula is lower than the midline of the optic nerve, it's not inverted. If there is a notch on the side of the image (square, triangle, or circle) then it's not inverted. If there is no notch, it's inverted. Like any real-world data set, you will encounter noise in both the images and labels. Images may contain artifacts, be out of focus, underexposed, or overexposed. A major aim of this competition is to develop robust algorithms that can function in the presence of noise and variation. https://i.imgur.com/Tmba2IF.png}, terms= {}, license= {}, superseded= {}, url= {https://www.kaggle.com/c/diabetic-retinopathy-detection} }

DeepLesion (10,594 CT scans with lesions) (Dataset)

@article{, title= {DeepLesion (10,594 CT scans with lesions)}, keywords= {}, author= {Ke Yan (National Institutes of Health Clinical Center)}, abstract= {## Introduction The DeepLesion dataset contains 32,120 axial computed tomography (CT) slices from 10,594 CT scans (studies) of 4,427 unique patients. There are 1–3 lesions in each image with accompanying bounding boxes and size measurements, adding up to 32,735 lesions altogether. The lesion annotations were mined from NIH’s picture archiving and communication system (PACS). Some meta-data are also provided. The contents include: - Folder “Images\_png”: png image files. We named each slice with the format “{patient index}\_{study index}\_{series index}\_{slice index}.png”, with the last underscore being / or \ to indicate sub-folders. The images are stored in unsigned 16 bit. One should subtract 32768 from the pixel intensity to obtain the original Hounsfield unit (HU) values. We provide not only the key CT slice that contains the lesion annotation, but also its 3D context (30mm extra slices above and below the key slice). Due to the large size of the data and the file size limit of the website, we packed them to 56 smaller zip files for downloading. - Key_slices.zip: key slices with overlaid lesion annotations for review purposes. - Folder “Key_slice_examples”: random image examples chosen from Key_slices.zip. - DL_info.csv: The annotations and meta-data. See Section “Annotations” below. ## Reference Ke Yan, Xiaosong Wang, Le Lu, Ronald M. Summers, "DeepLesion: Automated Mining of Large-Scale Lesion Annotations and Universal Lesion Detection with Deep Learning", Journal of Medical Imaging 5(3), 036501 (2018), doi: 10.1117/1.JMI.5.3.036501 ## Annotations In DL_info.csv, each row is the information of a lesion in DeepLesion. The meaning of the columns are: 1. File name. Please replace the last underscore with / or \ to indicate sub-folders. 2. Patient index starting from 1. 3. Study index for each patient starting from 1. There are 1~26 studies for each patient. 4. Series ID. 5. Slice index of the key slice containing the lesion annotation, starting from 1. 6. 8D vector, the image coordinates (in pixel) of the two RECIST diameters of the lesion. [x11, y11, x12, y12, x21, y21, x22, y22]. The first 4 coordinates are for the long axis. Please see our paper and its supplementary material for further explanation. 7. 4D vector, the bounding-box [x1, y1, x2, y2] of the lesion (in pixel) estimated from the RECIST diameters, see our paper 8. 2D vector, the lengths of the long and short axes. The unit is pixels. 9. The relative body position of the center of the lesion. The z-coordinates were predicted by the self-supervised body part regressor. See our paper for details. The coordinates are approximate and just for reference. 10. The type of the lesion. Types 1~8 correspond to bone, abdomen, mediastinum, liver, lung, kidney, soft tissue, and pelvis, respectively. See our paper for details. The lesion types are coarsely defined and just for reference. Only the lesions in the val and test sets were annotated with others denoted as -1. 11. This field is set to 1 if the annotation of this lesion is possibly noisy according to manual check. We found 35 noisy annotations out of 32,735 till now. 12. Slice range. Context slices neighboring to the key slice were provided in this dataset. For example, in the first lesion, the key slice is 109 and the slice range is 103~115, meaning that slices 103~115 are provided. For most lesions, we provide 30mm extra slices above and below the key slice, unless the long axis of the lesion is larger than this thickness (then we provide more) or the beginning or end of the volume is reached. 13. Spacing (mm per pixel) of the x, y, and z axes. The 3rd value is the slice interval, or the physical distance between two slices. 14. Image size. 15. The windowing (min~max) in Hounsfield unit extracted from the original DICOM file. 16. Patient gender. F for female and M for male. 17. Patient age. 18. Official randomly generated patient-level data split, train=1, validation=2, test=3. ## Applications DeepLesion is a large-scale dataset that contains a variety types of lesions. It can be used for lesion detection, classification, segmentation, retrieval, measurement, growth analysis, relationship mining between different lesions, etc. Limitations Since DeepLesion was mined from PACS, it has a few limitations: - DeepLesion contains only 2D diameter measurements and bounding-boxes of lesions. It has no lesion segmentation masks, 3D bounding-boxes, or fine-grained lesion types. Therefore, some applications (e.g. lesion segmentation) may need extra manual annotations. - Not all lesions were annotated in the images. Radiologists typically mark only representative lesions in each study. Therefore, some lesions remain unannotated. - According to manual examination, although most bookmarks represent abnormal findings or lesions, a small proportion of the bookmarks are actually measurement of normal structures, such as lymph nodes of normal size. https://i.imgur.com/AuNDBbz.png ## Acknowledgments This research was supported by the Intramural Research Program of the NIH Clinical Center. We thank NVIDIA for the donation of GPU cards. We thank our lab members Jiamin Liu, Yuxing Tang, and Youbao Tang for their help in preparing the dataset.}, terms= {}, license= {"usage of the data set is unrestricted"}, superseded= {}, url= {https://nihcc.app.box.com/v/DeepLesion} }

regnet.pkl (Dataset)

@article{, title= {regnet.pkl}, journal= {}, author= {Liu ZP, Wu C, Miao H, Wu H.}, year= {}, url= {http://www.regnetworkweb.org}, abstract= {In this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. }, keywords= {machine learning, Graph, Transcriptomics, Computational Biology, Gene Expression, Genomics, Graph Convolutions}, terms= {}, license= {}, superseded= {} }

genemania.pkl (Dataset)

@article{, title= {genemania.pkl}, journal= {}, author= {Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q}, year= {}, url= {}, abstract= {A pickled networkx file containing 16,300 human genes and their associations in an undirected graph with 264,657 edges. GeneMania (Warde-Farley et al., 2010) is a combination of previously published protein-protein interaction and co-expression graphs. }, keywords= {Graph, machine learning, Transcriptomics, Computational Biology, Gene Expression, Genomics, Graph Convolutions}, terms= {}, license= {}, superseded= {} }

Labeled Optical Coherence Tomography (OCT) (Dataset)

@article{, title= {Labeled Optical Coherence Tomography (OCT)}, keywords= {}, author= {}, abstract= {Dataset of validated OCT images described and analyzed in "Deep learning-based classification and referral of treatable human diseases". The OCT Images are split into a training set and a testing set of independent patients. OCT Images are labeled as (disease)-(randomized patient ID)-(image number by this patient) and split into 4 directories: CNV, DME, DRUSEN, and NORMAL. ``` 250 files in directory ./test/CNV 250 files in directory ./test/DME 250 files in directory ./test/DRUSEN 250 files in directory ./test/NORMAL 37205 files in directory ./train/CNV 11348 files in directory ./train/DME 8616 files in directory ./train/DRUSEN 26315 files in directory ./train/NORMAL ``` https://i.imgur.com/tsAGf0V.png ## Acknowledgements Data: https://data.mendeley.com/datasets/rscbjbr9sj/2 License: CC BY 4.0 ## Citation: Kermany D, Goldbaum M, Cai W et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018; 172(5):1122-1131. doi:10.1016/j.cell.2018.02.010. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5}, terms= {}, license= {CC BY 4.0}, superseded= {}, url= {https://data.mendeley.com/datasets/rscbjbr9sj/3} }

Chest X-Ray Images (Pediatric Pneumonia) (Dataset)

@article{, title= {Chest X-Ray Images (Pediatric Pneumonia)}, keywords= {radiology}, author= {}, abstract= {The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care. For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert. https://i.imgur.com/U7dBW7X.png ## Acknowledgements Data: https://data.mendeley.com/datasets/rscbjbr9sj/2 License: CC BY 4.0 ## Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5}, terms= {}, license= {CC BY 4.0}, superseded= {}, url= {https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/home} }

Indiana University - Chest X-Rays (PNG Images) (Dataset)

@article{, title= {Indiana University - Chest X-Rays (PNG Images)}, keywords= {radiology, chest x-ray}, author= {OpenI}, abstract= {1000 radiology reports for the chest x-ray images from the Indiana University hospital network. To identify images associated with the reports, use XML tag. More than one image could be associated with a report) https://i.imgur.com/5uR5snH.png}, terms= {}, license= {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License}, superseded= {}, url= {https://openi.nlm.nih.gov/faq.php} }

Indiana University - Chest X-Rays (XML Reports) (Dataset)

@article{, title= {Indiana University - Chest X-Rays (XML Reports)}, keywords= {chest x-ray, radiology}, author= {}, abstract= {1000 radiology reports for the chest x-ray images from the Indiana University hospital network. To identify images associated with the reports, use XML tag . More than one image could be associated with a report) https://i.imgur.com/PWo3x47.png}, terms= {}, license= {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License}, superseded= {}, url= {https://openi.nlm.nih.gov/faq.php} }

Zinc Molecule Dataset from Constrained Graph Variational Autoencoders for Molecule Design (Dataset)

@article{liu2018constrained, title= {Zinc Molecule Dataset from Constrained Graph Variational Autoencoders for Molecule Design}, author= {Liu, Qi and Allamanis, Miltiadis and Brockschmidt, Marc and Gaunt, Alexander L.}, journal= {The Thirty-second Conference on Neural Information Processing Systems}, year= {2018}, abstract= {ZINC is a free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable compounds in ready-to-dock, 3D formats. ZINC also contains over 750 million purchasable compounds you can search for analogs in under a minute. Sterling and Irwin, J. Chem. Inf. Model, 2015 http://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00559 This particular dataset comes from the paper Constrained Graph Variational Autoencoders for Molecule Design, Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt. }, keywords= {zinc, graph generation, molecule generation}, terms= {}, license= {}, superseded= {}, url= {https://github.com/Microsoft/constrained-graph-variational-autoencoder} }

The PatchCamelyon benchmark dataset (PCAM) (Dataset)

@article{, title= {The PatchCamelyon benchmark dataset (PCAM)}, keywords= {}, author= {Bas Veeling}, abstract= {The PatchCamelyon benchmark is a new and challenging image classification dataset. It consists of 327.680 color images (96 x 96px) extracted from histopathologic scans of lymph node sections. Each image is annoted with a binary label indicating presence of metastatic tissue. PCam provides a new benchmark for machine learning models: bigger than CIFAR10, smaller than imagenet, trainable on a single GPU. ## Why PCam Fundamental machine learning advancements are predominantly evaluated on straight-forward natural-image classification datasets. Think MNIST, CIFAR, SVHN. Medical imaging is becoming one of the major applications of ML and we believe it deserves a spot on the list of go-to ML datasets. Both to challenge future work, and to steer developments into directions that are beneficial for this domain. We think PCam can play a role in this. It packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task, akin to CIFAR-10 and MNIST. Models can easily be trained on a single GPU in a couple hours, and achieve competitive scores in the Camelyon16 tasks of tumor detection and WSI diagnosis. Furthermore, the balance between task-difficulty and tractability makes it a prime suspect for fundamental machine learning research on topics as active learning, model uncertainty and explainability. https://github.com/basveeling/pcam/raw/master/pcam.jpg }, terms= {}, license= {}, superseded= {}, url= {https://github.com/basveeling/pcam} }

BRATS2013 Tumor-NoTumor Dataset (T-NT) (Dataset)

@article{, title= {BRATS2013 Tumor-NoTumor Dataset (T-NT)}, keywords= {TNT}, author= {}, abstract= {This dataset (called T-NT) contains images which contain or do not contain a tumor along with a segmentation of brain matter and the tumor. The goal is that it can be used to simulate bias in data in a controlled fashion. # Dataset Construction The synthetic data of the BRATS2013 dataset is used to construct this dataset. Each brain contains a tumor but it is typically only on one side. Only the right side is taken in order to have examples that do not have tumors. Each image is filtered to ensure it has enough brain in the image (more than 30% of the pixels). If the tumor takes up at least 1% of the pixels in the brain then it is considered to have a tumor. Here is an snippet from the code used to construct the dataset: ``` def get_labels(rightside): met = {} met['brain'] = ( 1. * (rightside != 0).sum() / (rightside == 0).sum()) met['tumor'] = ( 1. * (rightside > 2).sum() / ((rightside != 0).sum() + 1e-10)) met['has_enough_brain'] = met['brain'] > 0.30 met['has_tumor'] = met['tumor'] > 0.01 return met ``` # File and Folder structure The files are organized as follows: PatientID-SlideNumber-HasTumor.png For example: ``` HG0011-118-False.png HG0015-65-True.png HG0019-95-False.png ``` The segmentation images are pixel values that correspond to the following 6 classes: ``` Non Tumor classes: 0, 10, 20 Tumor classes: 40 Unknown classes: 30, 50 ``` A Tumor example https://i.imgur.com/WIKFhO1.png A NoTumor example https://i.imgur.com/AbkTw5L.png The folders are divided into training or testing by patient. Then they are divided into flair, t1, and a segmentation image. ``` train (2125 images, 1421 tumor, 704 notumor) ├── flair ├── segmentation └── t1 holdout (1415 images, 1051 tumor, 364 notumor) ├── flair ├── segmentation └── t1 ``` Patients in training: ['HG0018' 'HG0019' 'HG0012' 'HG0013' 'HG0010' 'HG0011' 'HG0016' 'HG0017' 'HG0014' 'HG0015' 'HG0023' 'HG0022' 'HG0021' 'LG0005' 'LG0004' 'LG0007' 'LG0006' 'LG0001' 'LG0003' 'LG0002' 'LG0025' 'LG0024' 'LG0009' 'LG0022' 'LG0021' 'LG0020' 'HG0009' 'HG0008' 'HG0002' 'HG0025'] Patients in test: ['HG0001' 'HG0003' 'HG0024' 'HG0005' 'HG0004' 'HG0007' 'HG0006' 'HG0020' 'LG0023' 'LG0008' 'LG0016' 'LG0017' 'LG0014' 'LG0015' 'LG0012' 'LG0013' 'LG0010' 'LG0011' 'LG0018' 'LG0019'] Sample Flair Images | Tumor | NoTumor | |:----------:|:-------------:| | https://i.imgur.com/3305V4u.png | https://i.imgur.com/QDVB4fo.png| | https://i.imgur.com/kGHfa8Q.png | https://i.imgur.com/MKA9vxK.png| # Citation If you use this dataset, please cite: ``` Distribution Matching Losses Can Hallucinate Features in Medical Image Translation Joseph Paul Cohen, Margaux Luck, Sina Honari Medical Image Computing & Computer Assisted Intervention (MICCAI) https://arxiv.org/abs/1805.08841 ``` ``` @article{cohen2018distribution, author = {Cohen, Joseph Paul and Luck, Margaux and Honari, Sina}, journal = {Medical Image Computing & Computer Assisted Intervention (MICCAI)}, title = {Distribution Matching Losses Can Hallucinate Features in Medical Image Translation}, year = {2018} } ``` ## License The original files are shared with the following license so our dataset is shared with the same license. "Except where otherwise noted, content is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Switzerland License. http://creativecommons.org/licenses/by-nc-sa/3.0/ch/deed.en" The following papers describe the original dataset: Menze et al., The Multimodal Brain TumorImage Segmentation Benchmark (BRATS), IEEE Trans. Med. Imaging, 2015.Get the citation as BibTex Kistler et. al, The virtual skeleton database: an open access repository for biomedical research and collaboration. JMIR, 2013. (BibTex) }, terms= {}, license= {Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)}, superseded= {}, url= {https://github.com/ieee8023/dist-bias} }

Non-contrast head/brain CT CQ500 Dataset (Dataset)

@article{, title= {Non-contrast head/brain CT CQ500 Dataset}, keywords= {}, author= {Qure.ai}, abstract= {CQ500 dataset of 491 Computed tomography scans with 193,317 slices Anonymized dicoms for all the scans and the corresponding radiologists' reads. ![](https://i.imgur.com/wor2XEA.png) Paper: https://arxiv.org/abs/1803.05854}, terms= {}, license= {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License}, superseded= {}, url= {https://web.archive.org/web/20220816011051/http://headctstudy.qure.ai/} }

MICCAI_BraTS_2018_Data_Validation (Dataset)

@article{, title= {MICCAI_BraTS_2018_Data_Validation}, keywords= {}, author= {BraTS}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {}, year= {2018} }

MICCAI_BraTS_2018_Data_Training (Dataset)

@article{, title= {MICCAI_BraTS_2018_Data_Training}, journal= {}, author= {BraTS }, year= {}, url= {}, abstract= {https://i.imgur.com/iONFbKt.gif ``` ./HGG/Brats18_CBICA_AOO_1 ./HGG/Brats18_TCIA02_471_1 ./HGG/Brats18_CBICA_ARW_1 ./HGG/Brats18_CBICA_ASK_1 ./HGG/Brats18_TCIA08_105_1 ./HGG/Brats18_CBICA_AAB_1 ./HGG/Brats18_CBICA_ATV_1 ./HGG/Brats18_CBICA_ASA_1 ./HGG/Brats18_CBICA_AQN_1 ./HGG/Brats18_TCIA04_192_1 ./HGG/Brats18_2013_20_1 ./HGG/Brats18_TCIA01_147_1 ./HGG/Brats18_CBICA_APR_1 ./HGG/Brats18_TCIA02_321_1 ./HGG/Brats18_CBICA_AQD_1 ./HGG/Brats18_CBICA_ALX_1 ./HGG/Brats18_TCIA08_205_1 ./HGG/Brats18_CBICA_AQJ_1 ./HGG/Brats18_TCIA01_203_1 ./HGG/Brats18_2013_2_1 ./HGG/Brats18_CBICA_AUN_1 ./HGG/Brats18_TCIA02_300_1 ./HGG/Brats18_CBICA_ASE_1 ./HGG/Brats18_CBICA_ASO_1 ./HGG/Brats18_CBICA_ATX_1 ./HGG/Brats18_CBICA_AAL_1 ./HGG/Brats18_TCIA03_419_1 ./HGG/Brats18_CBICA_AXL_1 ./HGG/Brats18_CBICA_AQV_1 ./HGG/Brats18_CBICA_ALN_1 ./HGG/Brats18_TCIA06_165_1 ./HGG/Brats18_TCIA03_338_1 ./HGG/Brats18_TCIA01_378_1 ./HGG/Brats18_CBICA_ASY_1 ./HGG/Brats18_CBICA_AUR_1 ./HGG/Brats18_TCIA02_198_1 ./HGG/Brats18_2013_11_1 ./HGG/Brats18_CBICA_AAP_1 ./HGG/Brats18_CBICA_ATD_1 ./HGG/Brats18_TCIA02_171_1 ./HGG/Brats18_CBICA_ASW_1 ./HGG/Brats18_TCIA08_234_1 ./HGG/Brats18_TCIA03_257_1 ./HGG/Brats18_TCIA02_314_1 ./HGG/Brats18_CBICA_ABM_1 ./HGG/Brats18_TCIA03_138_1 ./HGG/Brats18_TCIA04_361_1 ./HGG/Brats18_CBICA_AQR_1 ./HGG/Brats18_TCIA05_478_1 ./HGG/Brats18_TCIA02_331_1 ./HGG/Brats18_TCIA02_135_1 ./HGG/Brats18_2013_23_1 ./HGG/Brats18_TCIA06_409_1 ./HGG/Brats18_2013_5_1 ./HGG/Brats18_CBICA_ALU_1 ./HGG/Brats18_CBICA_AYA_1 ./HGG/Brats18_CBICA_AQG_1 ./HGG/Brats18_TCIA02_322_1 ./HGG/Brats18_TCIA03_121_1 ./HGG/Brats18_CBICA_AXW_1 ./HGG/Brats18_CBICA_ANP_1 ./HGG/Brats18_TCIA01_186_1 ./HGG/Brats18_TCIA01_221_1 ./HGG/Brats18_TCIA08_167_1 ./HGG/Brats18_CBICA_ASH_1 ./HGG/Brats18_TCIA02_222_1 ./HGG/Brats18_TCIA05_444_1 ./HGG/Brats18_TCIA02_168_1 ./HGG/Brats18_TCIA03_498_1 ./HGG/Brats18_CBICA_ANZ_1 ./HGG/Brats18_TCIA06_332_1 ./HGG/Brats18_TCIA08_242_1 ./HGG/Brats18_CBICA_ARZ_1 ./HGG/Brats18_TCIA02_368_1 ./HGG/Brats18_CBICA_AOH_1 ./HGG/Brats18_TCIA02_226_1 ./HGG/Brats18_TCIA02_491_1 ./HGG/Brats18_TCIA02_309_1 ./HGG/Brats18_CBICA_AWH_1 ./HGG/Brats18_TCIA06_603_1 ./HGG/Brats18_TCIA08_280_1 ./HGG/Brats18_TCIA03_265_1 ./HGG/Brats18_TCIA05_396_1 ./HGG/Brats18_TCIA06_372_1 ./HGG/Brats18_2013_27_1 ./HGG/Brats18_TCIA02_117_1 ./HGG/Brats18_TCIA08_469_1 ./HGG/Brats18_CBICA_ARF_1 ./HGG/Brats18_CBICA_AUQ_1 ./HGG/Brats18_2013_18_1 ./HGG/Brats18_TCIA01_235_1 ./HGG/Brats18_TCIA03_375_1 ./HGG/Brats18_TCIA04_343_1 ./HGG/Brats18_2013_12_1 ./HGG/Brats18_TCIA04_328_1 ./HGG/Brats18_TCIA01_131_1 ./HGG/Brats18_TCIA06_247_1 ./HGG/Brats18_CBICA_AXO_1 ./HGG/Brats18_TCIA01_335_1 ./HGG/Brats18_TCIA01_150_1 ./HGG/Brats18_CBICA_BHM_1 ./HGG/Brats18_CBICA_AQU_1 ./HGG/Brats18_CBICA_AQQ_1 ./HGG/Brats18_CBICA_ABN_1 ./HGG/Brats18_TCIA01_425_1 ./HGG/Brats18_TCIA03_296_1 ./HGG/Brats18_TCIA08_218_1 ./HGG/Brats18_CBICA_AYW_1 ./HGG/Brats18_CBICA_AOP_1 ./HGG/Brats18_CBICA_AZD_1 ./HGG/Brats18_TCIA01_231_1 ./HGG/Brats18_TCIA04_149_1 ./HGG/Brats18_TCIA08_406_1 ./HGG/Brats18_CBICA_AOZ_1 ./HGG/Brats18_TCIA02_607_1 ./HGG/Brats18_CBICA_ABY_1 ./HGG/Brats18_CBICA_APZ_1 ./HGG/Brats18_2013_4_1 ./HGG/Brats18_CBICA_AMH_1 ./HGG/Brats18_CBICA_AWG_1 ./HGG/Brats18_2013_22_1 ./HGG/Brats18_TCIA01_411_1 ./HGG/Brats18_TCIA03_474_1 ./HGG/Brats18_TCIA02_473_1 ./HGG/Brats18_TCIA08_162_1 ./HGG/Brats18_CBICA_ASG_1 ./HGG/Brats18_CBICA_ATP_1 ./HGG/Brats18_TCIA01_499_1 ./HGG/Brats18_TCIA01_201_1 ./HGG/Brats18_2013_26_1 ./HGG/Brats18_TCIA08_436_1 ./HGG/Brats18_TCIA02_208_1 ./HGG/Brats18_CBICA_AWI_1 ./HGG/Brats18_TCIA02_608_1 ./HGG/Brats18_2013_13_1 ./HGG/Brats18_CBICA_ATF_1 ./HGG/Brats18_TCIA02_394_1 ./HGG/Brats18_CBICA_ANI_1 ./HGG/Brats18_2013_19_1 ./HGG/Brats18_TCIA04_437_1 ./HGG/Brats18_TCIA08_113_1 ./HGG/Brats18_CBICA_AQT_1 ./HGG/Brats18_CBICA_AXN_1 ./HGG/Brats18_TCIA04_479_1 ./HGG/Brats18_TCIA02_290_1 ./HGG/Brats18_CBICA_AXJ_1 ./HGG/Brats18_CBICA_AQZ_1 ./HGG/Brats18_CBICA_BHB_1 ./HGG/Brats18_CBICA_ABE_1 ./HGG/Brats18_TCIA08_278_1 ./HGG/Brats18_TCIA06_184_1 ./HGG/Brats18_CBICA_ABO_1 ./HGG/Brats18_CBICA_AQP_1 ./HGG/Brats18_CBICA_AVG_1 ./HGG/Brats18_TCIA01_401_1 ./HGG/Brats18_TCIA02_606_1 ./HGG/Brats18_TCIA03_199_1 ./HGG/Brats18_TCIA02_179_1 ./HGG/Brats18_CBICA_ANG_1 ./HGG/Brats18_TCIA02_377_1 ./HGG/Brats18_CBICA_ATB_1 ./HGG/Brats18_TCIA01_460_1 ./HGG/Brats18_2013_17_1 ./HGG/Brats18_CBICA_ASU_1 ./HGG/Brats18_TCIA08_319_1 ./HGG/Brats18_TCIA02_118_1 ./HGG/Brats18_TCIA01_412_1 ./HGG/Brats18_CBICA_AOD_1 ./HGG/Brats18_TCIA05_277_1 ./HGG/Brats18_CBICA_AYI_1 ./HGG/Brats18_TCIA02_283_1 ./HGG/Brats18_CBICA_APY_1 ./HGG/Brats18_TCIA02_455_1 ./HGG/Brats18_CBICA_BFP_1 ./HGG/Brats18_2013_7_1 ./HGG/Brats18_2013_21_1 ./HGG/Brats18_CBICA_AQO_1 ./HGG/Brats18_2013_3_1 ./HGG/Brats18_2013_25_1 ./HGG/Brats18_CBICA_AXQ_1 ./HGG/Brats18_CBICA_AME_1 ./HGG/Brats18_TCIA02_430_1 ./HGG/Brats18_TCIA04_111_1 ./HGG/Brats18_CBICA_AQA_1 ./HGG/Brats18_CBICA_AVV_1 ./HGG/Brats18_TCIA06_211_1 ./HGG/Brats18_CBICA_ASN_1 ./HGG/Brats18_TCIA01_180_1 ./HGG/Brats18_CBICA_AAG_1 ./HGG/Brats18_TCIA02_151_1 ./HGG/Brats18_TCIA01_429_1 ./HGG/Brats18_CBICA_BFB_1 ./HGG/Brats18_CBICA_AXM_1 ./HGG/Brats18_TCIA01_448_1 ./HGG/Brats18_CBICA_AVJ_1 ./HGG/Brats18_CBICA_ABB_1 ./HGG/Brats18_TCIA02_370_1 ./HGG/Brats18_2013_10_1 ./HGG/Brats18_TCIA01_190_1 ./HGG/Brats18_CBICA_AZH_1 ./HGG/Brats18_TCIA02_605_1 ./HGG/Brats18_TCIA01_390_1 ./HGG/Brats18_CBICA_ASV_1 ./HGG/Brats18_2013_14_1 ./HGG/Brats18_TCIA02_374_1 ./HGG/Brats18_CBICA_AYU_1 ./HGG/Brats18_TCIA02_274_1 ./HGG/Brats18_TCIA03_133_1 ./HGG/Brats18_CBICA_AQY_1 ./HGG/Brats18_CBICA_BHK_1 ./LGG/Brats18_TCIA10_639_1 ./LGG/Brats18_TCIA13_630_1 ./LGG/Brats18_2013_6_1 ./LGG/Brats18_TCIA13_615_1 ./LGG/Brats18_2013_8_1 ./LGG/Brats18_2013_24_1 ./LGG/Brats18_TCIA10_490_1 ./LGG/Brats18_TCIA10_637_1 ./LGG/Brats18_TCIA13_634_1 ./LGG/Brats18_TCIA10_346_1 ./LGG/Brats18_TCIA10_202_1 ./LGG/Brats18_TCIA09_312_1 ./LGG/Brats18_TCIA13_624_1 ./LGG/Brats18_TCIA10_442_1 ./LGG/Brats18_TCIA10_152_1 ./LGG/Brats18_TCIA13_645_1 ./LGG/Brats18_TCIA09_177_1 ./LGG/Brats18_TCIA10_629_1 ./LGG/Brats18_2013_15_1 ./LGG/Brats18_TCIA09_402_1 ./LGG/Brats18_TCIA10_408_1 ./LGG/Brats18_TCIA12_480_1 ./LGG/Brats18_2013_29_1 ./LGG/Brats18_TCIA10_241_1 ./LGG/Brats18_TCIA13_633_1 ./LGG/Brats18_TCIA09_493_1 ./LGG/Brats18_TCIA12_101_1 ./LGG/Brats18_TCIA12_470_1 ./LGG/Brats18_TCIA13_618_1 ./LGG/Brats18_TCIA09_451_1 ./LGG/Brats18_TCIA10_387_1 ./LGG/Brats18_TCIA09_141_1 ./LGG/Brats18_2013_1_1 ./LGG/Brats18_TCIA09_255_1 ./LGG/Brats18_TCIA10_130_1 ./LGG/Brats18_TCIA10_420_1 ./LGG/Brats18_TCIA10_393_1 ./LGG/Brats18_TCIA09_620_1 ./LGG/Brats18_TCIA10_351_1 ./LGG/Brats18_TCIA10_299_1 ./LGG/Brats18_TCIA13_642_1 ./LGG/Brats18_2013_16_1 ./LGG/Brats18_TCIA10_330_1 ./LGG/Brats18_TCIA13_623_1 ./LGG/Brats18_2013_28_1 ./LGG/Brats18_TCIA10_410_1 ./LGG/Brats18_TCIA10_282_1 ./LGG/Brats18_TCIA13_653_1 ./LGG/Brats18_TCIA10_261_1 ./LGG/Brats18_TCIA10_325_1 ./LGG/Brats18_2013_0_1 ./LGG/Brats18_TCIA12_298_1 ./LGG/Brats18_TCIA10_644_1 ./LGG/Brats18_TCIA10_625_1 ./LGG/Brats18_TCIA09_254_1 ./LGG/Brats18_TCIA10_175_1 ./LGG/Brats18_TCIA10_310_1 ./LGG/Brats18_TCIA10_640_1 ./LGG/Brats18_TCIA10_266_1 ./LGG/Brats18_TCIA10_632_1 ./LGG/Brats18_TCIA13_650_1 ./LGG/Brats18_TCIA10_307_1 ./LGG/Brats18_TCIA10_103_1 ./LGG/Brats18_TCIA10_413_1 ./LGG/Brats18_TCIA10_109_1 ./LGG/Brats18_TCIA12_249_1 ./LGG/Brats18_2013_9_1 ./LGG/Brats18_TCIA13_654_1 ./LGG/Brats18_TCIA09_428_1 ./LGG/Brats18_TCIA10_449_1 ./LGG/Brats18_TCIA13_621_1 ./LGG/Brats18_TCIA10_276_1 ./LGG/Brats18_TCIA09_462_1 ./LGG/Brats18_TCIA10_628_1 ./LGG/Brats18_TCIA12_466_1 ```}, keywords= {}, terms= {}, license= {}, superseded= {} }

Medical Segmentation Decathlon Datasets (Dataset)

@article{, title= {Medical Segmentation Decathlon Datasets}, keywords= {}, author= {}, abstract= {https://i.imgur.com/QqgA5n4.jpg With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalisability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML, would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. This challenge and dataset aims to provide such resource thorugh the open sourcing of large medical imaging datasets on several highly different tasks, and by standardising the analysis and validation process. ``` 4.6M ./Task06_Lung/labelsTr 5.7G ./Task06_Lung/imagesTr 2.9G ./Task06_Lung/imagesTs 8.6G ./Task06_Lung 240K ./Task05_Prostate/labelsTr 150M ./Task05_Prostate/imagesTr 79M ./Task05_Prostate/imagesTs 229M ./Task05_Prostate 15M ./Task01_BrainTumour/labelsTr 4.5G ./Task01_BrainTumour/imagesTr 2.7G ./Task01_BrainTumour/imagesTs 7.1G ./Task01_BrainTumour 8.6M ./Task07_Pancreas/labelsTr 7.6G ./Task07_Pancreas/imagesTr 3.9G ./Task07_Pancreas/imagesTs 12G ./Task07_Pancreas 388K ./Task02_Heart/labelsTr 249M ./Task02_Heart/imagesTr 186M ./Task02_Heart/imagesTs 435M ./Task02_Heart 8.7M ./Task08_HepaticVessel/labelsTr 5.8G ./Task08_HepaticVessel/imagesTr 3.0G ./Task08_HepaticVessel/imagesTs 8.8G ./Task08_HepaticVessel 1.3M ./Task09_Spleen/labelsTr 1.1G ./Task09_Spleen/imagesTr 461M ./Task09_Spleen/imagesTs 1.5G ./Task09_Spleen 14M ./Task10_Colon/labelsTr 4.0G ./Task10_Colon/imagesTr 1.9G ./Task10_Colon/imagesTs 5.9G ./Task10_Colon 30M ./Task03_Liver/labelsTr 19G ./Task03_Liver/imagesTr 8.6G ./Task03_Liver/imagesTs 27G ./Task03_Liver 1.1M ./Task04_Hippocampus/labelsTr 19M ./Task04_Hippocampus/imagesTr 8.8M ./Task04_Hippocampus/imagesTs 29M ./Task04_Hippocampus 71G . ``` Competition site: https://decathlon-10.grand-challenge.org/}, terms= {}, license= {CC-BY-SA 4.0}, superseded= {}, url= {http://medicaldecathlon.com/} }

MoNuSeg Training Data - Multi-organ nuclei segmentation from H&E stained histopathological images (Dataset)

@article{, title= {MoNuSeg Training Data - Multi-organ nuclei segmentation from H&E stained histopathological images}, keywords= {}, author= {}, abstract= {Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Techniques that accurately segment nuclei in diverse images spanning a range of patients, organs, and disease states, can significantly contribute to the development of clinical and medical research software. Once accurately segmented, nuclear morphometric and appearance features such as density, nucleus-to-cytoplasm ratio, average size, and pleomorphism can be used to assess not only cancer grades but also for predicting treatment effectiveness. Identifying different types of nuclei based on their segmentation can also yield information about gland shapes, which, for example, is important for cancer grading. This challenge will showcase the best nuclei segmentation techniques that will work on a diverse set of H&E stained histology images obtained from different hospitals spanning multiple patients and organs. This will enable training and testing of readily usable (or generalized) nuclear segmentation softwares. The dataset for this challenge was obtained by carefully annotating tissue images of several patients with tumors of different organs and who were diagnosed at multiple hospitals. This dataset was created by downloading H&E stained tissue images captured at 40x magnification from TCGA archive. H&E staining is a routine protocol to enhance the contrast of a tissue section and is commonly used for tumor assessment (grading, staging, etc.). Given the diversity of nuclei appearances across multiple organs and patients, and the richness of staining protocols adopted at multiple hospitals, the training datatset will enable the development of robust and generalizable nuclei segmentation techniques that will work right out of the box. ![](https://i.imgur.com/2p2GMWt.png) #### Citation Request N. Kumar, R. Verma, S. Sharma, S. Bhargava, A. Vahadane and A. Sethi, "A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology," in IEEE Transactions on Medical Imaging, vol. 36, no. 7, pp. 1550-1560, July 2017}, terms= {}, license= {Attribution 3.0 Unported (CC BY 3.0)}, superseded= {}, url= {https://monuseg.grand-challenge.org/} }

Human MCF7 cells – compound-profiling experiment (BBBC021v1) (Dataset)

@article{, title= {Human MCF7 cells – compound-profiling experiment (BBBC021v1)}, keywords= {Medical, Biology}, author= {The Broad Institute}, abstract= {![](https://i.imgur.com/nilVHAT.png) ![](https://i.imgur.com/8dcAHs0.png) ### Description of the biological application Phenotypic profiling attempts to summarize multiparametric, feature-based analysis of cellular phenotypes of each sample so that similarities between profiles reflect similarities between samples. Profiling is well established for biological readouts such as transcript expression and proteomics. Image-based profiling, however, is still an emerging technology. This image set provides a basis for testing image-based profiling methods wrt. to their ability to predict the mechanisms of action of a compendium of drugs. The image set was collected using a typical set of morphological labels and uses a physiologically relevant p53-wildtype breast-cancer model system (MCF-7) and a mechanistically distinct set of targeted and cancer-relevant cytotoxic compounds that induces a broad range of gross and subtle phenotypes. ### Images The images are of MCF-7 breast cancer cells treated for 24 h with a collection of 113 small molecules at eight concentrations. The cells were fixed, labeled for DNA, F-actin, and Β-tubulin, and imaged by fluorescent microscopy as described [Caie et al. Molecular Cancer Therapeutics, 2010]. There are 39,600 image files (13,200 fields of view imaged in three channels) in TIFF format. We provide the images in 55 ZIP archives, one for each microtiter plate. ### Metadata The file BBBC021_v1_image.csv contains the metadata, with the following fields: ``` TableNumber ImageNumber Image_FileName_DAPI Image_PathName_DAPI Image_FileName_Tubulin Image_PathName_Tubulin Image_FileName_Actin Image_PathName_Actin Image_Metadata_Plate_DAPI Image_Metadata_Well_DAPI Replicate Image_Metadata_Compound Image_Metadata_Concentration ``` ### Ground truth B A subset of the compound-concentrations have been identified as clearly having one of 12 different primary mechanims of action. mechanistic classes were selected so as to represent a wide cross-section of cellular morphological phenotypes. The differences between phenotypes were in some cases very subtle: we were only able to identify 6 of the 12 mechanisms visually; the remainder were defined based on the literature. The file BBBC021_v1_moa.csv contains the mechanisms of action of 103 compound-concentrations (38 compounds at 1–7 concentrations each). The fields are: ``` compound concentration moa ``` ### Recommended citation "We used image set BBBC021v1 [Caie et al., Molecular Cancer Therapeutics, 2010], available from the Broad Bioimage Benchmark Collection [Ljosa et al., Nature Methods, 2012]." }, terms= {}, license= {}, superseded= {}, url= {https://data.broadinstitute.org/bbbc/BBBC021/} }

LiTS – Liver Tumor Segmentation Challenge (LiTS17) (Dataset)

@article{, title= {LiTS – Liver Tumor Segmentation Challenge (LiTS17)}, keywords= {}, author= {Patrick Christ}, abstract= {The liver is a common site of primary (i.e. originating in the liver like hepatocellular carcinoma, HCC) or secondary (i.e. spreading to the liver like colorectal cancer) tumor development. Due to their heterogeneous and diffusive shape, automatic segmentation of tumor lesions is very challenging. Until now, only interactive methods achieved acceptable results segmenting liver lesions. With our challenge we encourage researchers to develop automatic segmentation algorithms to segment liver lesions in contrast-enhanced abdominal CT scans. The data and segmentations are provided by various clinical sites around the world. The training data set contains 130 CT scans and the test data set 70 CT scans. The challenge is organised in conjunction with ISBI 2017 and MICCAI 2017. For MICCAI 2017 we added tasks for liver segmentation and tumor burden estimation. ![](https://i.imgur.com/ia2qGlH.png) ![](https://i.imgur.com/eDN20ck.png) Paper reference: https://arxiv.org/abs/1901.04056 }, terms= {}, license= {https://creativecommons.org/licenses/by-nc-nd/4.0/}, superseded= {}, url= {https://competitions.codalab.org/competitions/17094} }

LUng Nodule Analysis (LUNA16) All Images (Dataset)

@article{, title= {LUng Nodule Analysis (LUNA16) All Images}, keywords= {radiology}, author= {Consortium for Open Medical Image Computing}, abstract= {| ![](https://i.imgur.com/8Oolu7D.png) | ![](https://i.imgur.com/5WsoKqU.png) | |-- |- | Lung cancer is the leading cause of cancer-related death worldwide. Screening high risk individuals for lung cancer with low-dose CT scans is now being implemented in the United States and other countries are expected to follow soon. In CT lung cancer screening, many millions of CT scans will have to be analyzed, which is an enormous burden for radiologists. Therefore there is a lot of interest to develop computer algorithms to optimize screening. A vital first step in the analysis of lung cancer screening CT scans is the detection of pulmonary nodules, which may or may not represent early stage lung cancer. Many Computer-Aided Detection (CAD) systems have already been proposed for this task. The LUNA16 challenge will focus on a large-scale evaluation of automatic nodule detection algorithms on the LIDC/IDRI data set. The LIDC/IDRI data set is publicly available, including the annotations of nodules by four radiologists. The LUNA16 challenge is therefore a completely open challenge. We have tracks for complete systems for nodule detection, and for systems that use a list of locations of possible nodules. We provide this list to also allow teams to participate with an algorithm that only determines the likelihood for a given location in a CT scan to contain a pulmonary nodule. ### Motivation Lung cancer is the leading cause of cancer-related death worldwide. The National Lung Screening Trial (NLST), a randomized control trial in the U.S. including more than 50,000 high-risk subjects, showed that lung cancer screening using annual low-dose computed tomography (CT) reduces lung cancer mortality by 20% in comparison to annual screening with chest radiography [1]. In 2013, the U.S. Preventive Services Task Force (USPSTF) has given low-dose CT screening a grade B recommendation for high-risk individuals [2] and early 2015, the U.S. Centers for Medicare and Medicaid Services (CMS) has approved CT lung cancer screening for Medicare recipients. As a result of these developments, lung cancer screening programs using low-dose CT are being implemented in the United States and other countries. Computer-aided detection (CAD) of pulmonary nodules could play an important role when screening is implemented on a large scale. Large evaluation studies investigating the performance of different state-of-the-art CAD systems are scarce. Therefore, we organize a novel CAD detection challenge using a large public LIDC-IDRI dataset. The detailed description of the challenge is now available in this article. We believe that this challenge is important for a reliable comparison of CAD algorithms and to encourage rapid development of new algorithms using state-of-the-art computer vision technology. ### Challenge tracks We invite the research community to participate in one or two of the following challenge tracks: 1. Nodule detection (NDET) Using raw CT scans, the goal is to identify locations of possible nodules, and to assign a probability for being a nodule to each location. The pipeline typically consists of two stages: candidate detection and false positive reduction. 2. False positive reduction (FPRED) Given a set of candidate locations, the goal is to assign a probability for being a nodule to each candidate location. Hence, one could see this as a classification task: nodule or not a nodule. Candidate locations will be provided in world coordinates. This set detects 1,162/1,186 nodules. ### Open challenge LUNA16 is a completely open challenge. This means that unlike other challenges, images and reference standard are publicly available. The goal of LUNA16 is to provide an opportunity for participants to test their algorithm on common database with a standardized evaluation protocol. With the spirit of speeding-up scientic progress, the results listed on the website can be used as an indication on how well state-of-the-art CAD algorithms perform. We hope LUNA16 will yield several results that are worthwhile for the CAD research community. We are committed to maintain this site as a public repository of benchmark results for nodule detection on a common database in the spirit of cooperative scientific progress. In return, we ask everyone who uses this site to respect the rules below. ### Rules The following rules apply to those who register a team and download the data: The downloaded data sets or any data derived from these data sets, may not be given or redistributed under any circumstances to persons not belonging to the registered team. All information entered when registering a team, including the name of the contact person, the affiliation (institute, organization or company the team's contact person works for) and the e-mail address must be complete and correct. In other words, anonymous registration is not allowed. If you want to submit anonymous, for example because you want to submit your results to a conference that requires anonymous submission, please contact the organizers. The LUNA16 organizers reserve the right to request a pdf file describing the system to accompany the submitted result. The organizers may refuse to evaluate systems whose description does not meet minimal requirements. Results uploaded to this website will be made publicly available on this site (see the Results Section), and by submitting results, you grant us permission to do so. Obviously, teams maintain full ownership and rights to the method. Teams must notify the maintainers of this site about any publication that is (partly) based on the data on this site, in order for us to maintain a list of publications associated with the LUNA16 study. ### References [1] Aberle D. R., Adams A. M., Berg C. D., Black W. C., Clapp J. D., Fagerstrom R. M., Gareen I. F., Gatsonis C., Marcus P. M., and Sicks J. D. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 365:395–409, 2011. [2] Moyer VA, U.S. Preventive Services Task Force. Screening for lung cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med, 160:330-338 2014. [3] Armato SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 38:915–931, 2011. ### Organizers Colin Jacobs (Radboud University Medical Center, Nijmegen, The Netherlands) Arnaud Arindra Adiyoso Setio (Radboud University Medical Center, Nijmegen, The Netherlands) Alberto Traverso (Polytechnic University of Turin and Turin Section of INFN, Turin, Italy) Bram van Ginneken (Radboud University Medical Center, Nijmegen, The Netherlands) ![](https://i.imgur.com/8Oolu7D.png) ![](https://i.imgur.com/5WsoKqU.png) }, terms= {}, license= {}, superseded= {}, url= {} }

A collection of sport activity datasets with an emphasis on powermeter data (Dataset)

@article{, title= {A collection of sport activity datasets with an emphasis on powermeter data}, keywords= {sport, dataset, triathlon, cycling}, journal= {Technical report, 2017}, author= {Iztok Fister Jr. and Samo Rauter and Dusan Fister and Iztok Fister}, year= {2017}, url= {}, license= {}, abstract= {}, superseded= {}, terms= {} }

Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Image Data (Dataset)

@article{, title= {Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Image Data}, keywords= {}, journal= {}, author= {}, year= {}, url= {https://wiki.cancerimagingarchive.net/display/Public/APOLLO}, license= {}, abstract= {This data collection consists of images and associated data acquired from the APOLLO Network. The Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) network is a collaboration between NCI, the Department of Defense (DoD), and the Department of Veterans Affairs (VA) to incorporate proteogenomics into patient care as a way of looking beyond the genome, to the activity and expression of the proteins that the genome encodes. The emerging field of proteogenomics aims to better predict how patients will respond to therapy by screening their tumors for both genetic abnormalities and protein information, an approach that has been made possible in recent years due to advances in proteomic technology. | Detailed Description | | |----------------------------|-----| | Image Size (GB) | 2.6 | | Modalities | PET, CT, MRI, MRA + others | | Number of Images | 6203 | | Number of Patients | 7 | | Number of Series | 43 | | Number of Studies | 36 | # Citation request Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. ![](https://i.imgur.com/TAshQmM.png) }, superseded= {}, terms= {} }

MRI Lesion Segmentation in Multiple Sclerosis Database (Dataset)

@article{, title= {MRI Lesion Segmentation in Multiple Sclerosis Database}, keywords= {}, author= {}, abstract= {MRI MS DB Description: In the IMT-Segmentation folder there are 38 folders representing data for each patient 38patients). In each patient folder we have: 1) MRI TIFF Images from first and second examination (0 months, 6-12 months) 2) Lesion segmentations (*.plq files). The delineation/s can be loaded into matlab i.e load(file.plq, '-.mat'); Then points can be drawn on the image. %How to load the point deliniations into MATLAB and plot them to the image. load('IM_00031_1.plq','-mat'); a=imread('IM_00031.tif'); figure, imshow(a), hold on, plot(yi,xi, 'LineWidth', 3), hold off; Further download information for the database may be obtained by contacting Prof. Christos P. Loizou (panloicy@logosnet.cy.net). ![](https://i.imgur.com/K2JypfI.png) ## Citation request 1. C.P. Loizou, V. Murray, M.S. Pattichis, I. Seimenis, M. Pantziaris, C.S. Pattichis, ìMulti-scale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis in brain MRI images,î IEEE Trans. Inform. Tech. Biomed., vol. 15, no. 1, pp. 119-129, 2011. 2. C.P. Loizou, E.C. Kyriacou, I. Seimenis, M. Pantziaris, S. Petroudi, M. Karaolis, C.S. Pattichis, ìBrain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability,î Intelligent Decision Technologies Journal (IDT), vol. 7, pp. 3-10, 2013. 3. C.P. Loizou, M. Pantziaris, C.S. Pattichis, I. Seimenis, ìBrain MRI Image normalization in texture analysis of multiple sclerosisî, J. Biomed. Graph. & Comput., vol. 3, no.1, pp. 20-34, 2013. 4. C.P. Loizou, S. Petroudi, I. Seimenis, M. Pantziaris, C.S. Pattichis, Quantitative texture analysis of brain white matter lesions derived from T2-weighted MR images in MS patients with clinically isolated syndromeî, J. Neuroradiol., acepted. }, terms= {}, license= {}, superseded= {}, url= {http://www.medinfo.cs.ucy.ac.cy/doc/Publications/Datasets/} }

MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation (BraTS2013) (Dataset)

@article{, title= {MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation (BraTS2013)}, keywords= {}, journal= {}, author= {}, year= {2013}, url= {http://martinos.org/qtim/miccai2013/data.html}, license= {Creative Commons Attribution-NonCommercial 3.0 license}, abstract= {A publicly available set of training data can be downloaded for algorithmic tweaking and tuning from the Virtual Skeleton Database. The training data consists of multi-contrast MR scans of 30 glioma patients (both low-grade and high-grade, and both with and without resection) along with expert annotations for "active tumor" and "edema". For each patient, T1, T2, FLAIR, and post-Gadolinium T1 MR images are available. All volumes were linearly co-registered to the T1 contrast image, skull stripped, and interpolated to 1mm isotropic resolution. No attempt was made to put the individual patients in a common reference space. The MR scans, as well as the corresponding reference segmentations, are distributed in the ITK- and VTK-compatible MetaIO file format. Patients with high- and low-grade gliomas have file names "BRATS_HG" and "BRATS_LG", respectively. All images are stored as signed 16-bit integers, but only positive values are used. The manual segmentations (file names ending in "_truth.mha") have only five intensity levels: 1 for Non-brain, non-tumor, necrosis, cyst, hemorrhage, 2 for Surrounding edema, 3 for Non-enhancing tumor, 4 for enhancing tumor core and 0 for everything else. Detailed technical documentation on the used MetaIO file format is available here. The training data also contains simulated images for 25 high-grade and 25 low-grade glioma subjects. These simulated images closely follow the conventions used for the real data, except that their file names start with "SimBRATS"; they are all in BrainWeb space; and their MR scans and ground truth segmentations are stored using unsigned 16 bit and unsigned 8 bit integers, respectively. Details on the simulation method are available here. Testing data A set of independent testing data will be provided on the day of the challenge itself. This testing data will be similar to the training data, except that the reference segmentation will not be made publicly available. ![](https://i.imgur.com/aSB7Y0r.png)}, superseded= {}, terms= {The BRATS training and testing data are made freely available through the Creative Commons Attribution-NonCommercial 3.0 license. Please include the following language in any work using the BRATS data: "Brain tumor image data used in this work were obtained from the NCI-MICCAI 2013 Challenge on Multimodal Brain Tumor Segmentation (http://martinos.org/qtim/miccai2013/index.html) organized by K. Farahani, M. Reyes,B. Menze, E. Gerstner, J. Kirby and J. Kalpathy-Cramer . The challenge database contains fully anonymized images from the following institutions: ETH Zurich, University of Bern, University of Debrecen, and University of Utah and publicly available images from the Cancer Imaging Archive (TCIA)."} }

Caudate Segmentation Evaluation 2007 (CAUSE07) (Dataset)

@article{, title= {Caudate Segmentation Evaluation 2007 (CAUSE07)}, keywords= {}, journal= {}, author= {B. van Ginneken and T. Heimann and M. Styner}, year= {}, url= {https://cause07.grand-challenge.org/home/}, license= {}, abstract= {CAUSE07 is a competition that was held as part of the workshop 3D Segmentation in the Clinic: A Grand Challenge, on October 26, 2007 in conjunction with MICCAI 2007. The goal of this competition was to compare different algorithms to segment the caudate nucleaus from brain MRI scans. Through this website, the competition continues. You can browse the results of various systems, and read papers and descriptions about the methods that have been applied to the CAUSE07 data set. If you want to join the competition, you can register a team, download training and test data, and submit the results of your own algorithms, provided you adhere to and agree with the rules. More information is available in the answers to frequently asked questions. ![](https://i.imgur.com/Yu4YFY1.gif) ## Citation Request "3D Segmentation in the Clinic: A Grand Challenge", B. van Ginneken, T. Heimann, and M. Styner. In: T. Heimann, M. Styner, B. van Ginneken (Eds.): 3D Segmentation in the Clinic: A Grand Challenge, pp. 7-15, 2007.}, superseded= {}, terms= {} }

Malignant lymphoma classification (Dataset)

@article{, title= {Malignant lymphoma classification}, keywords= {}, author= {Elaine Jaffe (National Cancer Institute) and Nikita Orlov (National Institute on Aging)}, abstract= {Malignant lymphoma is a cancer affecting lymph nodes. Three types of malignant lymphoma are represented in the set: CLL (chronic lymphocytic leukemia), FL (follicular lymphoma), and MCL (mantle cell lymphoma). The ability to distinguish classes of lymphoma from biopsies sectioned and stained with Hematoxylin/Eosin (H+E) would allow for more consistent and less demanding diagnosis of this disease. Only the most expert pathologists specializing in these types of lymphomas are able to consistently and accurately classify these three lymphoma types from H+E-stained biopsies. The standard practice is to use class-specific probes in order to distinguish these classes reliably. The dataset presented is a collection of samples prepared by different pathologists at different sites. There is a large degree of staining variation that one would normally expect from such samples. A randomly selected image from each class: ![](https://i.imgur.com/qoo1AAM.png)}, terms= {}, license= {}, superseded= {}, url= {https://ome.grc.nia.nih.gov/iicbu2008/lymphoma/index.html} }

Breast Cancer Cell Segmentation (Dataset)

@article{, title= {Breast Cancer Cell Segmentation}, keywords= {}, author= {Elisa Drelie Gelasca and Jiyun Byun and Boguslaw Obara and B.S. Manjunath}, abstract= {There are about 58 H&E stained histopathology images used in breast cancer cell detection with associated ground truth data available. Routine histology uses the stain combination of hematoxylin and eosin, commonly referred to as H&E. These images are stained since most cells are essentially transparent, with little or no intrinsic pigment. Certain special stains, which bind selectively to particular components, are be used to identify biological structures such as cells. In those images, the challenging problem is cell segmentation for subsequent classification in benign and malignant cells. The ground truth have been obtained for one image containing benign cells. | Image: |Ground Truth: | |---|---| | ![](https://i.imgur.com/haa5X8O.png) | ![](https://i.imgur.com/gqBikTa.png) | All images: ![](https://i.imgur.com/QM22bG2.png)}, terms= {}, license= {}, superseded= {}, url= {http://bioimage.ucsb.edu/research/bio-segmentation} }

Electron Microscopy (CA1 hippocampus) Dataset (Dataset)

@article{, title= {Electron Microscopy (CA1 hippocampus) Dataset}, keywords= {}, author= {}, abstract= {The dataset available for download on this webpage represents a 5x5x5µm section taken from the CA1 hippocampus region of the brain, corresponding to a 1065x2048x1536 volume. The resolution of each voxel is approximately 5x5x5nm. The data is provided as multipage TIF files that can be loaded in Fiji. ![](https://i.imgur.com/rTCKgHn.png) ![](https://i.imgur.com/DkDkaMH.gif) We annotated mitochondria in two sub-volumes. Each sub-volume consists of the first 165 slices of the 1065x2048x1536 image stack. The volume used for training our algorithm in the publications mentionned at the bottom of this page is the top part while the bottom part was used for testing. Although our line of research was primarily motivated by the need to accurately segment mitochondria and synapses, other structures are of interest for neuroscientists such as vesicles or cell boundaries. This dataset was acquired by Graham Knott and Marco Cantoni at EPFL. It is made publicly available in the hope of encouraging similar sharing of useful data amongst researchers and also accelerating neuroscientific research. For further information, please visit http://cvlab.epfl.ch/research/medical/em/mitochondria. ``` total 3.7G 124M testing_groundtruth.tif 124M testing.tif 124M training_groundtruth.tif 124M training.tif 3.2G volumedata.tif ``` ### References A. Lucchi Y. Li and P. Fua, Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets, Conference on Computer Vision and Pattern Recognition, 2013. A. Lucchi, K.Smith, R. Achanta, G. Knott, P. Fua, Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features, IEEE Transactions on Medical Imaging, Vol. 30, Nr. 11, October 2011. }, terms= {}, license= {}, superseded= {}, url= {https://cvlab.epfl.ch/data/em} }

NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories (Dataset)

@article{, title= {NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories}, journal= {}, author= {National Institutes of Health - Clinical Center}, year= {}, url= {https://www.nih.gov/news-events/news-releases/nih-clinical-center-provides-one-largest-publicly-available-chest-x-ray-datasets-scientific-community}, abstract= {![](https://i.imgur.com/1InHgLs.png) (1, Atelectasis; 2, Cardiomegaly; 3, Effusion; 4, Infiltration; 5, Mass; 6, Nodule; 7, Pneumonia; 8, Pneumothorax; 9, Consolidation; 10, Edema; 11, Emphysema; 12, Fibrosis; 13, Pleural_Thickening; 14 Hernia) ### Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neural networks for thorax disease recognition is severely limited by the availability of only 4143 frontal view images [3] (Openi is the previous largest publicly available chest X-ray dataset to date). In this database, we provide an enhanced version (with 6 more disease categories and more images as well) of the dataset used in the recent work [1] which is approximately 27 times of the number of frontal chest x-ray images in [3]. Our dataset is extracted from the clinical PACS database at National Institutes of Health Clinical Center and consists of ~60% of all frontal chest x-rays in the hospital. Therefore we expect this dataset is significantly more representative to the real patient population distributions and realistic clinical diagnosis challenges, than any previous chest x-ray datasets. Of course, the size of our dataset, in terms of the total numbers of images and thorax disease frequencies, would better facilitate deep neural network training [2]. Refer to [1] on the details of how the dataset is extracted and image labels are mined through natural language processing (NLP). ### Details: ChestX-ray dataset comprises 112,120 frontal-view X-ray images of 30,805 unique patients with the text-mined fourteen disease image labels (where each image can have multi-labels), mined from the associated radiological reports using natural language processing. Fourteen common thoracic pathologies include Atelectasis, Consolidation, Infiltration, Pneumothorax, Edema, Emphysema, Fibrosis, Effusion, Pneumonia, Pleural_thickening, Cardiomegaly, Nodule, Mass and Hernia, which is an extension of the 8 common disease patterns listed in our CVPR2017 paper. Note that original radiology reports (associated with these chest x-ray studies) are not meant to be publicly shared for many reasons. The text-mined disease labels are expected to have accuracy >90%.Please find more details and benchmark performance of trained models based on 14 disease labels in our arxiv paper: https://arxiv.org/abs/1705.02315 ### Contents: 1. 112,120 frontal-view chest X-ray PNG images in 1024*1024 resolution (under images folder) 2. Meta data for all images (Data_Entry_2017.csv): Image Index, Finding Labels, Follow-up #, Patient ID, Patient Age, Patient Gender, View Position, Original Image Size and Original Image Pixel Spacing. 3. Bounding boxes for ~1000 images (BBox_List_2017.csv):Image Index, Finding Label, Bbox[x, y, w, h]. [x y] are coordinates of each box's topleft corner. [w h] represent the width and height of each box. If you find the dataset useful for your research projects, please cite our CVPR 2017 paper:Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, MohammadhadiBagheri, Ronald M. Summers.ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases, IEEE CVPR, pp. 3462-3471,2017 ``` @InProceedings{wang2017chestxray,author = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald}, title = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases}, booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)}, pages = {3462--3471}, year = {2017}} ``` ### Questions/Comments: (xiaosong.wang@nih.gov; le.lu@nih.gov; rms@nih.gov) ### Limitations: 1. The image labels are NLP extracted so there would be some erroneous labels but the NLP labelling accuracy is estimated to be >90%. 2. Very limited numbers of disease region bounding boxes. 3. Chest x-ray radiology reports are not anticipated to be publicly shared. Parties who use this public dataset are encouraged to share their “updated” image labels and/or new bounding boxes in their own studied later, maybe through manual annotation. ### Acknowledgement: This work was supported by the Intramural Research Program of the NIH Clinical Center (clinicalcenter.nih.gov) and National Library of Medicine (www.nlm.nih.gov). We thank NVIDIA Corporation for the GPU donations. ### Reference: [1] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, MohammadhadiBagheri, Ronald Summers, ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common ThoraxDiseases, IEEE CVPR, pp. 3462-3471,2017 [2] Hoo-chang Shin, Kirk Roberts, Le Lu, Dina Demner-Fushman, Jianhua Yao, Ronald M. Summers, Learning to Read Chest X-Rays: Recurrent Neural CascadeModel for Automated Image Annotation, IEEE CVPR, pp. 2497-2506, 2016 [3] Open-i: An open access biomedical search engine. https: //openi.nlm.nih.gov ![](https://www.nih.gov/sites/default/files/styles/featured_media_breakpoint-medium/public/news-events/news-releases/2017/20170927-lung-mass.jpg?itok=wSFXjg6d×tamp=1506520936) }, keywords= {}, terms= {}, license= {"The usage of the data set is unrestricted"}, superseded= {} }

MICCAI 2015 Challenge on Multimodal Brain Tumor Segmentation (BraTS2015) (Dataset)

@article{, title= {MICCAI 2015 Challenge on Multimodal Brain Tumor Segmentation (BraTS2015)}, keywords= {}, journal= {}, author= {}, year= {2015}, url= {http://braintumorsegmentation.org/}, license= {Creative Commons Attribution-NonCommercial 3.0 license. (CC BY NC SA 3.0)}, abstract= {Brain tumor image data used in this article were obtained from the MICCAI Challenge on Multimodal Brain Tumor Segmentation. The challenge database contain fully anonymized images from the Cancer Imaging Archive. 1 for necrosis 2 for edema 3 for non-enhancing tumor 4 for enhancing tumor 0 for everything else ``` here are 3 requirements for the successfull upload and validation of your segmentation: Use the MHA filetype to store your segmentations (not mhd) [use short or ushort if you experience any upload problems] Keep the same labels as the provided truth.mha (see above) Name your segmentations according to this template: VSD.your_description.###.mha replace the ### with the ID of the corresponding Flair MR images. This allows the system to relate your segmentation to the correct training truth. Download an example list for the training data and testing data. ``` ![](https://i.imgur.com/umg5BKD.png) ### Publications B. H. Menze et al., "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)," in IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 1993-2024, Oct. 2015. doi: 10.1109/TMI.2014.2377694 http://ieeexplore.ieee.org/document/6975210/ Kistler et. al, The virtual skeleton database: an open access repository for biomedical research and collaboration. JMIR, 2013.}, superseded= {}, terms= {} }

Non-Small Cell Lung Cancer CT Scan Dataset (NSCLC-Radiomics-Genomics) (Dataset)

@article{, title= {Non-Small Cell Lung Cancer CT Scan Dataset (NSCLC-Radiomics-Genomics)}, keywords= {}, journal= {}, author= {}, year= {}, url= {http://doi.org/10.7937/K9/TCIA.2015.L4FRET6Z}, license= {Creative Commons Attribution 3.0 Unported License}, abstract= {This collection contains images from 89 non-small cell lung cancer (NSCLC) patients that were treated with surgery. For these patients pretreatment CT scans, gene expression, and clinical data are available. This dataset refers to the Lung3 dataset of the study published in Nature Communications. In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. The dataset described here (Lung3) was used to investigate the association of radiomic imaging features with gene-expression profiles. The Lung2 dataset used for training the radiomic biomarker and consisting of 422 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics. For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School (hugo_aerts@dfci.harvard.edu). Gene-expression Data Corresponding microarray data acquired for the imaging samples are available at National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (Link to GEO: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE58661). The patient names used to identify the cases on GEO are identical to those used in the DICOM files on TCIA and in the clinical data spreadsheet. Clinical Data Corresponding clinical data can be found here: Lung3.metadata.xls. Please note that survival time is measured in days from start of treatment. DICOM patients names are identical in TCIA and clinical data file. ![](https://wiki.cancerimagingarchive.net/download/thumbnails/16056856/image2014-6-30%2014%3A56%3A33.png) ### Publications Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Cavalho, S., … Lambin, P. (2014, June 3). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications. Nature Publishing Group. http://doi.org/10.1038/ncomms5006 }, superseded= {}, terms= {} }

Ischemic Stroke Lesion Segmentation Challenge 2017 (ISLES2017) (Dataset)

@article{, title= {Ischemic Stroke Lesion Segmentation Challenge 2017 (ISLES2017)}, keywords= {}, journal= {}, author= {}, year= {2017}, url= {http://www.isles-challenge.org/}, license= {Open Database License}, abstract= {Ischemic Stroke Lesion Segmentation (ISLES), a medical image segmentation challenge at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2017. On the SMIR, you can register for the challenge, download the test data and submit your results. For more information, visit the official ISLES homepage under www.isles-challenge.org. ### THE ISLES CHALLENGE This challenge for stroke lesions segmentation has been very popular the past two years (2015, 2016) and yielded various methods, that help to tackle important challenges of modern stroke imaging analysis. This year the challenge provides acute stroke imaging scans and manually outlined lesions on follow-up scans. ### HOW IT WORKS If you are interested in participating, you are invited to download the training set, including both MRI scans as well as the corresponding expert segmentations of stroke lesions. This will allow you to validate and optimise your method as much as you favour. Shortly before MICCAI 2017 will take place, a set of test cases will be released of which participants will be asked to run their algorithm on and upload their segmentation results in form of binary image maps. To complete a successful participation, participants will need to submit an abstract, describing the employed method. The organizers will then evaluate each case and establish a ranking of the participating teams. All results will be presented during SWITCH at MICCAI 2017 and will be discussed with invited experts and all workshop attendees. Each team will have the opportunity to present their submitted method as a poster, while selected teams will be asked to give a brief presentation detailing their approach. Eventually, submissions will be included in the workshops LNCS post-proceedings and potentially compiled for a high-impact journal paper to summarise and present the findings. ### Please cite the challenge article if you use the data: Oskar Maier et al. ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI Medical Image Analysis, Available online 21 July 2016, ISSN 1361-8415 http://dx.doi.org/10.1016/j.media.2016.07.009. Kistler et al. The virtual skeleton database: an open access repository for biomedical research and collaboration. JMIR, 2013 http://doi.org//10.2196/jmir.2930 }, superseded= {}, terms= {} }

NIH Pancreas-CT Dataset (Dataset)

@article{, title= {NIH Pancreas-CT Dataset}, keywords= {}, journal= {}, author= {Holger R. Roth and Amal Farag and Evrim B. Turkbey and Le Lu and Jiamin Liu and Ronald M. Summers. }, year= {}, url= {http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU}, license= {Creative Commons Attribution 3.0 Unported License}, abstract= {### Summary The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects. Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy. The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions. Subjects' ages range from 18 to 76 years with a mean age of 46.8 ± 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 − 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage). A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist. The images were processed into nii files using the following script: ``` for i in `ls . | grep PAN`; do echo $i; dcm2niix -vox 1 -z y -o ./data/ -m y -s y -f %n $i done ``` ### Citation Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. ### Examples ![](https://i.imgur.com/4aZNgw6.gifv) ![](https://i.imgur.com/kfhhH7x.png) ![](https://i.imgur.com/kGbz9hl.png) }, superseded= {}, terms= {} }

Human acute monocytic leukemia (Dataset)

@article{, title= {Human acute monocytic leukemia}, keywords= {}, journal= {}, author= {Antony C.S. Chan}, year= {}, url= {http://dx.doi.org/10.1038/srep44608}, license= {MIT License}, abstract= {Complete dataset of the imaging flow cytometry of the human acute monocytic leukemia (THP-1) cells acquired by ultrafast optical time-stretch microscopy technique. Published in: Antony C. S. Chan, Ho-Cheung Ng, Sharat C. V. Bogaraju, Hayden K. H. So, Edmund Y. Lam & Kevin K. Tsia, "All-passive pixel super-resolution of time-stretch imaging" Scientific Reports 7, 44608 (2017) http://dx.doi.org/10.1038/srep44608 Preprint: https://arxiv.org/abs/1610.05802 To access the serial-temporal line-scans of the cellular images in MATLAB: trace8192 = h5read('leukemia_20161201.h5', '/raw2016Dec1_2224', [1,1], [16,8192/16]); This will load the first 8192 photodetector samples from the dataset.}, superseded= {}, terms= {Copyright (c) 2016, Antony C. S. Chan Department of Electrical & Electronic Engineering, The University of Hong Kong All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.} }

VGG Cell Dataset from Learning To Count Objects in Images (Dataset)

@article{, title= {VGG Cell Dataset from Learning To Count Objects in Images }, keywords= {}, journal= {}, author= {Lempitsky, V. and Zisserman, A.}, booktitle= {Advances in Neural Information Processing Systems}, year= {2010}, url= {http://www.robots.ox.ac.uk/~vgg/research/counting/index_org.html}, license= {}, abstract= {![](https://i.imgur.com/ydlsPEh.png) We generated a dataset of 200 images, and used random subsets of the first 100 images to perform training and parameter validations, and the second 100 images to test the counting accuracy. Below, we show some representative results for cell counting for the previously unseen images ### Acknowledgements This work is a part of the EU VisRec project (ERC grant VisRec no. 228180). }, superseded= {}, terms= {} }

Open Payments Dataset - 2014 Program Year (Dataset)

@article{, title= {Open Payments Dataset - 2014 Program Year }, keywords= {}, year= {2014}, url= {https://www.cms.gov/OpenPayments/Explore-the-Data/Data-Overview.html}, author= {U.S. Centers for Medicare & Medicaid Services}, abstract= {Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known as third party payments) There are three distinct ways for you to review and search the data (and remember, you can view the summary data dashboard for an overview of published data): The Open Payments Final Rule §403.910 provides applicable manufacturers and applicable GPO's the opportunity to request a delay in publication for a period not to exceed four calendar years after the date the payment or other transfer of value was made, or upon the approval, licensure or clearance of the covered drug, device, biological, or medical supply by the FDA.}, terms= {} }

Open Payments Dataset - 2013 Program Year (Dataset)

@article{, title= {Open Payments Dataset - 2013 Program Year }, keywords= {}, year={2013}, url= {https://www.cms.gov/OpenPayments/Explore-the-Data/Data-Overview.html}, author= {U.S. Centers for Medicare & Medicaid Services}, abstract= {Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known as third party payments) There are three distinct ways for you to review and search the data (and remember, you can view the summary data dashboard for an overview of published data): The Open Payments Final Rule §403.910 provides applicable manufacturers and applicable GPO's the opportunity to request a delay in publication for a period not to exceed four calendar years after the date the payment or other transfer of value was made, or upon the approval, licensure or clearance of the covered drug, device, biological, or medical supply by the FDA.}, terms= {} }

Gland Segmentation in Histology Images Challenge (GlaS) Dataset (Dataset)

@article{, title= {Gland Segmentation in Histology Images Challenge (GlaS) Dataset}, keywords= {}, journal= {}, author= {Korsuk Sirinukunwattana}, year= {}, url= {http://www2.warwick.ac.uk/fac/sci/dcs/research/combi/research/bic/glascontest/}, license= {}, abstract= {![](http://www2.warwick.ac.uk/fac/sci/dcs/research/combi/research/bic/glascontest/glas3resize2.png) "We aim to bring together researchers who are interested in the gland segmentation problem, to validate the performance of their existing or newly invented algorithms on the same standard dataset. In this challenge, we will provide the participants with images of Haematoxylin and Eosin (H&E) stained slides, consisting of a wide range of histologic grades." ![](https://i.imgur.com/GzSJCu4.png) ## Introduction Glands are important histological structures which are present in most organ systems as the main mechanism for secreting proteins and carbohydrates. It has been shown that malignant tumours arising from glandular epithelium, also known as adenocarcinomas, are the most prevalent form of cancer. The morphology of glands has been used routinely by pathologists to assess the degree of malignancy of several adenocarcinomas, including prostate, breast, lung, and colon. Accurate segmentation of glands is often a crucial step to obtain reliable morphological statistics. Nonetheless, the task by nature is very challenging due to the great variation of glandular morphology in different histologic grades. Up until now, the majority of studies focus on gland segmentation in healthy or benign samples, but rarely on intermediate or high grade cancer, and quite often, they are optimised to specific datasets. In this challenge, participants are encouraged to run their gland segmentation algorithms on images of Hematoxylin and Eosin (H&E) stained slides, consisting of a variety of histologic grades. The dataset is provided together with ground truth annotations by expert pathologists. The participants are asked to develop and optimise their algorithms on the provided training dataset, and validate their algorithm on the test dataset. ## Data Description The challenge will be conducted on a dataset, acquired by a team of pathologists at the University Hospitals Coventry and Warwickshire, UK. Details of the dataset are as follows. | Dataset | Warwick-QU | |----------------------------|------------| | Cancer Type | Colorectal Cancer | | Resolution/ | 20X (0.62005 \mu{m}/pixel) | | Scanner | Zeiss MIRAX MIDI | | Number of Images | 165 | | Format | bmp | The composition of the dataset is as follows. | Split | Warwick-QU | |----------|----------------------------| | Training | benign : 37 malignant : 48 | | Test | benign : 37 malignant : 43 | The ground truth for each image in the training dataset is stored in a BMP file, one ground truth object per label. ## Challenge Tasks After registration, the team will receive a username and password for downloading the training datasets. Each team are asked to submit a short paper, which includes a description of their segmentation algorithm and some preliminary results on the training dataset. See submission section for more details. Teams that have submitted a short paper will be invited to present their work at GlaS challenge at MICCAI 2015. The test dataset will be made available upon the acceptance of your invitation. The organisers will evaluate the performance of a segmentation algorithm based on the test datasets and announce the final competition result at the GlaS Challenge event. }, superseded= {}, terms= {The dataset used in this competition is provided for research purposes only. Commercial uses are not allowed. If you intend to publish research work that uses this dataset, you must cite our review paper to be published after the competition K. Sirinukunwattana, J. P. W. Pluim, H. Chen, X Qi, P. Heng, Y. Guo, L. Wang, B. J. Matuszewski, E. Bruni, U. Sanchez, A. Böhm, O. Ronneberger, B. Ben Cheikh, D. Racoceanu, P. Kainz, M. Pfeiffer, M. Urschler, D. R. J. Snead, N. M. Rajpoot, "Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest" http://arxiv.org/abs/1603.00275 [Preprint] AND the following paper, wherein the same dataset was first used: K. Sirinukunwattana, D.R.J. Snead, N.M. Rajpoot, "A Stochastic Polygons Model for Glandular Structures in Colon Histology Images," in IEEE Transactions on Medical Imaging, 2015 doi: 10.1109/TMI.2015.2433900} }

Water microdroplet dataset (Dataset)

@article{, title= {Water microdroplet dataset}, keywords= {}, journal= {}, author= {}, year= {}, url= {http://dx.doi.org/10.1038/srep44608}, license= {MIT License}, abstract= {Complete dataset of the imaging flow cytometry of the water in silicone oil microdroplets acquired by ultrafast optical time-stretch microscopy technique. Published in: Antony C. S. Chan, Ho-Cheung Ng, Sharat C. V. Bogaraju, Hayden K. H. So, Edmund Y. Lam & Kevin K. Tsia, "All-passive pixel super-resolution of time-stretch imaging" Scientific Reports 7, 44608 (2017) http://dx.doi.org/10.1038/srep44608 Preprint: https://arxiv.org/abs/1610.05802}, superseded= {}, terms= {Copyright (c) 2016, Antony C. S. Chan Department of Electrical & Electronic Engineering, The University of Hong Kong All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.} }

Scendesmus dataset (Dataset)

@article{, title= {Scendesmus dataset}, keywords= {}, journal= {}, author= {Antony C. S. Chan}, year= {}, url= {http://dx.doi.org/10.1038/srep44608}, license= {MIT License}, abstract= {Complete dataset of the imaging flow cytometry of the ptyphoplankton (species: scenedesmus) cell colonies acquired by ultrafast optical time-stretch microscopy technique. Published in: Antony C. S. Chan, Ho-Cheung Ng, Sharat C. V. Bogaraju, Hayden K. H. So, Edmund Y. Lam & Kevin K. Tsia, "All-passive pixel super-resolution of time-stretch imaging" Scientific Reports 7, 44608 (2017) http://dx.doi.org/10.1038/srep44608 Preprint: https://arxiv.org/abs/1610.05802 Data hierachy: /sample_interval: sampling interval setting of the oscilloscope = 1/(5GHz) (unit: second) /datetime: date and time of the experiment /rawdata: serial-temporal imaging signal of each cell sample (dimensions = 5000 x 250001) /im_LR: images at lower pixel resolution, rendered at effective sampling rate = 5GHz (dimensions = 5000 x 109 x 42) /im_HR: images at lower pixel resolution, rendered at effective sampling rate = 20GHz (dimensions = 5000 x 109 x 42) /featureSet: table of extracted feature metrics (e.g. bounding rectangle dimensions, angle, Histogram of gradients...), indexed by the cell ID.}, superseded= {}, terms= {Copyright (c) 2016, Antony C. S. Chan Department of Electrical & Electronic Engineering, The University of Hong Kong All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.} }

OpenMIIR RawEEG v1.0 (Dataset)

@article{OpenMIIR-RawEEG_v1, title = {OpenMIIR RawEEG v1.0}, journal = {}, author = {Sebastian Stober and Avital Sternin and Adrian M. Owen and Jessica A. Grahn}, year = {2015}, url = {https://github.com/sstober/openmiir}, license = {ODC PDDL}, abstract = {Music imagery information retrieval (MIIR) systems may one day be able to recognize a song just as we think of it. As a step towards such technology, we are presenting a public domain dataset of electroencephalography (EEG) recordings taken during music perception and imagination. We acquired this data during an ongoing study that so far comprised 10 subjects listening to and imagining 12 short music fragments - each 7s-16s long - taken from well-known pieces. These stimuli were selected from different genres and systematically span several musical dimensions such as meter, tempo and the presence of lyrics. This way, various retrieval and classification scenarios can be addressed. The dataset is primarily aimed to enable music information retrieval researchers interested in these new MIIR challenges to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. We also hope that the OpenMIIR dataset will facilitate a stronger interdisciplinary collaboration between music information retrieval researchers and neuroscientists.} }