collection curated by sparrow https://academictorrents.com/collection/new-collection 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) Sample segmentations of microaneurysms (scaled down) Paper: https://academictorrents.com/download/3bb974ffdad31f9df9d26a63ed2aea2f1d789405 @article{, title= {UN corpus - training-parallel-un.tgz (ES-EN, FR-EN)}, keywords= {}, author= {WMT13}, abstract= {}, terms= {}, license= {}, superseded= {}, url= {} } https://academictorrents.com/download/e4dc3c28d6035a64af928dbdcbc8d6cc0d62d39c ## Introduction The DeepLesion dataset contains 32,120 axial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. https://academictorrents.com/download/de50f4d4aa3d028944647a56199c07f5fa6030ff The Blackbird unmanned aerial vehicle (UAV) dataset is a large-scale indoor dataset collected using a custom-built quadrotor platform for use in evaluation of agile perception. The dataset contains over 10 hours of flight data from 168 flights over 17 flight trajectories and 5 environments at velocities up to 8.0 m/s. Each flight includes sensor data from 120 Hz stereo and downward-facing photorealistic virtual cameras, 100 Hz IMU, 190 Hz motor speed sensors, and 360 Hz millimeter-accurate motion capture ground truth. Camera images for each flight were photorealistically rendered using FlightGoggles across a variety of environments to facilitate experimentation of perception algorithms. The dataset is available at . # Citation https://academictorrents.com/download/eb542a231dbeb2125e4ec88ddd18841a867c2656 34,686,770 Amazon reviews from 6,643,669 users on 2,441,053 products, from the Stanford Network Analysis Project (SNAP). This full dataset contains 600,000 training samples and 130,000 testing samples in each class. https://academictorrents.com/download/66ddbb6d5f49aa6c36a01ca5e814f1beef00b5b7 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://academictorrents.com/download/274be65156ed14828fb7b30b82407a2417e1924a Spoken Wikipedia 2005-2018 in MP3 format —- en/ English Spoken Wikipedia There are 857 (of 1,300) spoken articles in English. See list of audio articles: —- ru/ Russian Spoken Wikipedia There are 238 spoken articles in Russian. See list of audio articles: —- Bonus: + uk/ 1 audio article of Ukrainian Wikipedia (Music of Ukraine); + de/ 3 audio articles of German Wikipedia (Alexander Pushkin, T-60, Afghanischer Burgerkrieg (1989-2001)). P.S. If you want, I can add the torrent with the same files in OGG format. https://academictorrents.com/download/5d2a7304089a97cecb5de3f055495ed65013c968 We propose an artificial intelligence challenge to design algorithms that assist people who are blind to overcome their daily visual challenges. For this purpose, we introduce the VizWiz dataset, which originates from a natural visual question answering setting where blind people each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. Our proposed challenge addresses the following two tasks for this dataset: (1) predict the answer to a visual question and (2) predict whether a visual question cannot be answered. Ultimately, we hope this work will educate more people about the technological needs of blind people while providing an exciting new opportunity for researchers to develop assistive technologies that eliminate accessibility barriers for blind people. VizWiz v1.0 dataset download: 20,000 training image/question pairs 200,000 training answer/answer confidence pairs 3,173 image/question pairs 31,730 validation answer/answer confidence pairs 8,000 image/question pairs Python API to read and visualize the VizWiz dataset Python challenge evaluation code ![]() ### Publications Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, and Jeffrey P. Bigham. "VizWiz Grand Challenge: Answering Visual Questions from Blind People." IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. Jeffrey P. Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C. Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samuel White, and Tom Yeh. "VizWiz: Nearly Real-time Answers to Visual Questions." ACM User Interface Software and Technology Symposium (UIST), 2010. https://academictorrents.com/download/b633e14aa084fab57f20ad0b4612e0932ae1f2dc Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright. https://academictorrents.com/download/53555c69e3799d876159d7290ea60e56b35e36a9 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. ![]() ![]() Paper reference: https://academictorrents.com/download/27772adef6f563a1ecc0ae19a528b956e6c803ce 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: | |—-|—-| | ![]() | ![]() | All images: ![]() https://academictorrents.com/download/b79869ca12787166de88311ca1f28e3ebec12dec This is a training data set that can be used for the GANGogh machine learning model. Once downloaded, modify the styles variable in tflib/wikiartGenre.py as follows: https://academictorrents.com/download/1d154cde2fab9ec8039becd03d9bb877614d351b Competitors will receive an orthorectified color image, Digital Surface Model (DSM), and Digital Terrain Model (DTM) for each geographic area of interest (AOI). The DSM indicates the height of the earth, with objects such as buildings and trees included. The DTM indicates only the height of the ground. Both should be expected to include some errors, and errors may be expected to be similar in the provisional and sequestered data sets. The difference in the DSM and DTM indicates height of objects above ground. All input files provided are raster GeoTIFF images. Ground truth building labels will also be provided for a subset of the data to be used for training ![]() ![]() https://academictorrents.com/download/4ccd3743861d827ac80f0d2b234d7fcfdad2a31d ![]() (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: ### 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 https://academictorrents.com/download/557481faacd824c83fbf57dcf7b6da9383b3235a ### 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://academictorrents.com/download/80ecfefcabede760cdbdf63e38986501f7becd49 Aesthetic Visual Analysis (AVA) contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style for high-level image quality categorization. https://academictorrents.com/download/71631f83b11d3d79d8f84efe0a7e12f0ac001460 Reddit Comments from 2005-12 to 2017-03 Downloaded from . You can find a current list of SHA-sums there to verify this torrent s downloads. Intended use is for scientific / non-commercial purposes. Example code to work with the data: https://academictorrents.com/download/85a5bd50e4c365f8df70240ffd4ecc7dec59912b First Full Dataset Release - Udacity/Didi $100k Challenge One of the most important aspects of operating an autonomous vehicle is understanding the surrounding environment in order to make safe decisions. Udacity and Didi Chuxing are partnering together to provide incentive for students to come up with the best way to detect obstacles using camera and LIDAR data. This challenge will allow for pedestrian, vehicle, and general obstacle detection that is useful to both human drivers and self-driving car systems. Competitors will need to process LIDAR and Camera frames to output a set of obstacles, removing noise and environmental returns. Participants will be able to build on the large body of work that has been put into the Kitti datasets and challenges, using existing techniques and their own novel approaches to improve the current state-of-the-art. Specifically, students will be competing against each other in the Kitti Object Detection Evaluation Benchmark. While a current leaderboard exists for academic publications, Udacity and Didi will be hosting our own leaderboard specifically for this challenge, and we will be using the standard object detection development kit that enables us to evaluate approaches as they are done in academia and industry. IMPORTANT NOTICE There are some major differences between this Udacity dataset and the Kitti datasets. It is important to note that recorded positions are recorded with respect to the base station, not the capture vehicle. The NED positions in the ‘rtkfix’ topic are therefore in relation to a FIXED POINT, NOT THE CAPTURE OR OBSTACLE VEHICLES. The relative positions can be calculated easily, as the NED frame is cartesian space, not polar. The XML tracklet files will, however, be in the frame of the capture vehicle. This means that the capture vehicle is also included in the recorded positions, and is denoted by the ROS topic /gps/rtkfix in this first dataset. The single obstacle vehicle in this dataset is located in the obs1/ topic namespace, but this will be changed to /obstacles/obstacle_name in future releases to accommodate the creation of XML tracklet files for multiple obstacles. Orientation of obstacles are not evaluated in Round 1, but will be evaluated in Round 2. The pose section of the ROS bags included in this release IS NOT A VALID QUATERNION, and does not represent either the pose of the capture vehicle or the obstacle. There is no XML tracklet file included with these datasets. They will be released as soon as they are available, in conjunction with the opening of the online leaderboard. https://academictorrents.com/download/76352487923a31d47a6029ddebf40d9265e770b5