CAMELYON17 (202 files)
testing/patients/ 3.77GB
testing/patients/ 19.28GB
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testing/patients/ 4.80GB
testing/patients/ 22.95GB
testing/patients/ 21.75GB
testing/patients/ 13.61GB
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testing/patients/ 17.27GB
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testing/patients/ 13.23GB
testing/patients/ 18.10GB
testing/patients/ 4.13GB
testing/patients/ 4.15GB
testing/patients/ 14.44GB
testing/patients/ 9.53GB
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testing/patients/ 13.84GB
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testing/patients/ 13.61GB
testing/patients/ 3.05GB
testing/patients/ 13.60GB
testing/patients/ 4.69GB
testing/patients/ 9.25GB
testing/patients/ 15.85GB
testing/patients/ 15.00GB
testing/patients/ 15.56GB
testing/patients/ 8.55GB
testing/patients/ 23.89GB
testing/patients/ 4.23GB
testing/patients/ 20.92GB
testing/patients/ 10.57GB
testing/patients/ 3.72GB
testing/patients/ 9.51GB
testing/patients/ 14.15GB
testing/patients/ 17.91GB
testing/patients/ 18.38GB
testing/patients/ 18.91GB
testing/patients/ 22.54GB
testing/patients/ 11.06GB
testing/patients/ 12.59GB
testing/patients/ 23.80GB
training/center_0/ 7.39GB
training/center_0/ 6.44GB
training/center_0/ 10.28GB
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training/center_0/ 10.98GB
training/center_1/ 21.97GB
training/center_1/ 19.45GB
training/center_1/ 22.31GB
training/center_1/ 14.88GB
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training/center_1/ 19.12GB
training/center_1/ 20.10GB
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training/center_1/ 25.98GB
training/center_1/ 27.96GB
training/center_1/ 15.66GB
training/center_1/ 20.16GB
training/center_2/ 15.47GB
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training/center_2/ 18.87GB
training/center_2/ 10.45GB
training/center_2/ 10.54GB
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training/center_2/ 15.87GB
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training/center_2/ 10.06GB
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training/center_2/ 15.31GB
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training/center_2/ 16.08GB
training/center_2/ 11.66GB
training/center_3/ 12.25GB
training/center_3/ 12.06GB
training/center_3/ 13.61GB
training/center_3/ 10.38GB
training/center_3/ 15.20GB
training/center_3/ 10.68GB
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training/center_3/ 19.11GB
training/center_3/ 11.88GB
training/center_3/ 14.85GB
training/center_3/ 13.49GB
training/center_3/ 9.71GB
training/center_3/ 9.84GB
training/center_3/ 14.20GB
training/center_3/ 7.95GB
training/center_3/ 11.91GB
training/center_3/ 8.49GB
training/center_3/ 12.29GB
training/center_3/ 11.31GB
training/center_3/ 14.36GB
training/center_4/ 5.38GB
training/center_4/ 4.47GB
training/center_4/ 3.27GB
training/center_4/ 2.14GB
training/center_4/ 5.98GB
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training/center_4/ 4.04GB
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training/center_4/ 5.84GB
training/center_4/ 4.45GB
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training/center_4/ 3.73GB
training/center_4/ 4.12GB
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training/center_4/ 4.12GB
training/center_4/ 4.25GB
training/center_4/ 5.57GB
training/center_4/ 4.88GB
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Type: Dataset

title= {CAMELYON17},
keywords= {},
author= {},
abstract= {# CAMELYON17 Data Set
## Overview
Built on the success of its predecessor, CAMELYON17 is the second grand challenge in pathology organised by the [Computational Pathology Group]( of the Radboud University Medical Center (Radboudumc) in Nijmegen, The Netherlands.

The goal of this challenge is to evaluate new and existing algorithms for automated detection and classification of breast cancer metastases in whole-slide images of histological lymph node sections. This task has high clinical relevance and would normally require extensive microscopic assessment by pathologists. The presence of metastases in lymph nodes has therapeutic implications for breast cancer patients. Therefore, an automated solution would hold great promise to reduce the workload of pathologists while at the same time reduce the subjectivity in diagnosis.

For the complete description of the challenge and the data set please visit the [challenge]( website.

## Data
### Images
The data in this challenge contains a total of 1000 whole-slide images (WSIs) of sentinel lymph node from 5 different medical centers from The Netherlands: Radboud University Medical Center in Nijmegen, Canisius-Wilhelmina Hospital in Nijmegen, University Medical Center Utrecht, Rijnstate Hospital in Arnhem, and Laboratorium Pathologie Oost-Nederland in Hengelo.

The data set is divided into training and testing sets with 20 patients from each center in both sets. For each patient the shared 5 whole-slide images are zipped together into a single ZIP file. The patient pN-stages and the slide-level labels in the training set are shared in the *stage_labels.csv* file.

The slides are converted to generic [TIFF]( (Tagged Image File Format) using an open-source file converter, part of the [ASAP]( package.

### Annotations
From each center 10 slides are exhaustively annotated and the annotations are shared in XML format. The XML files are compatible with the [ASAP]( software. You may download this software and visualize the annotations overlaid on the whole slide image.

The provided XML files may have two groups of annotations ("metastases", or "normal") which can be accessed from the "PartOfGroup" attribute of the Annotation node in the XML file. Annotations belonging to group "metastases" represent tumor areas and annotations within group "normal" are non-tumor areas which have been cut-out from the original annotations in the "metastases" group.
terms= {},
license= {},
superseded= {},
url= {}