CAMELYON17 dataset
Peter Bandi

CAMELYON17 (1156 files)
annotations/patient_004_node_4.xml 2.65kB
annotations/patient_009_node_1.xml 28.58kB
annotations/patient_010_node_4.xml 61.14kB
annotations/patient_012_node_0.xml 26.91kB
annotations/patient_015_node_1.xml 25.92kB
annotations/patient_015_node_2.xml 8.64kB
annotations/patient_016_node_1.xml 1.01kB
annotations/patient_017_node_1.xml 1.57kB
annotations/patient_017_node_2.xml 82.32kB
annotations/patient_017_node_4.xml 21.76kB
annotations/patient_020_node_2.xml 8.80kB
annotations/patient_020_node_4.xml 62.02kB
annotations/patient_021_node_3.xml 7.31kB
annotations/patient_022_node_4.xml 126.44kB
annotations/patient_024_node_1.xml 6.15kB
annotations/patient_024_node_2.xml 3.55kB
annotations/patient_034_node_3.xml 158.65kB
annotations/patient_036_node_3.xml 17.09kB
annotations/patient_038_node_2.xml 12.15kB
annotations/patient_039_node_1.xml 17.21kB
annotations/patient_040_node_2.xml 3.96kB
annotations/patient_041_node_0.xml 1.76kB
annotations/patient_042_node_3.xml 114.21kB
annotations/patient_044_node_4.xml 44.20kB
annotations/patient_045_node_1.xml 13.27kB
annotations/patient_046_node_3.xml 28.59kB
annotations/patient_046_node_4.xml 90.82kB
annotations/patient_048_node_1.xml 8.33kB
annotations/patient_051_node_2.xml 273.16kB
annotations/patient_052_node_1.xml 133.90kB
annotations/patient_060_node_3.xml 1.48kB
annotations/patient_061_node_4.xml 13.53kB
annotations/patient_062_node_2.xml 22.46kB
annotations/patient_064_node_0.xml 2.77kB
annotations/patient_066_node_2.xml 7.12kB
annotations/patient_067_node_4.xml 13.61kB
annotations/patient_068_node_1.xml 4.35kB
annotations/patient_072_node_0.xml 7.78kB
annotations/patient_073_node_1.xml 123.16kB
annotations/patient_075_node_4.xml 92.06kB
annotations/patient_080_node_1.xml 10.15kB
annotations/patient_081_node_4.xml 11.07kB
annotations/patient_086_node_0.xml 2.78kB
annotations/patient_086_node_4.xml 2.68kB
annotations/patient_087_node_0.xml 2.42kB
annotations/patient_088_node_1.xml 9.71kB
annotations/patient_089_node_3.xml 6.04kB
annotations/patient_092_node_1.xml 31.96kB
annotations/patient_096_node_0.xml 147.95kB
annotations/patient_099_node_4.xml 38.75kB
checksums.md5 71.10kB
evaluation/evaluate.py 4.25kB
evaluation/example.csv 17.47kB
images/patient_000_node_0.tif 2.84GB
images/patient_000_node_1.tif 2.21GB
images/patient_000_node_2.tif 2.31GB
images/patient_000_node_3.tif 2.21GB
images/patient_000_node_4.tif 1.91GB
images/patient_001_node_0.tif 2.23GB
images/patient_001_node_1.tif 1.39GB
images/patient_001_node_2.tif 1.41GB
images/patient_001_node_3.tif 3.56GB
images/patient_001_node_4.tif 2.04GB
images/patient_002_node_0.tif 3.14GB
images/patient_002_node_1.tif 2.22GB
images/patient_002_node_2.tif 3.15GB
images/patient_002_node_3.tif 3.37GB
images/patient_002_node_4.tif 2.33GB
images/patient_003_node_0.tif 2.28GB
images/patient_003_node_1.tif 2.72GB
images/patient_003_node_2.tif 2.07GB
images/patient_003_node_3.tif 3.46GB
images/patient_003_node_4.tif 2.13GB
images/patient_004_node_0.tif 2.56GB
images/patient_004_node_1.tif 1.66GB
images/patient_004_node_2.tif 3.01GB
images/patient_004_node_3.tif 3.38GB
images/patient_004_node_4.tif 3.25GB
images/patient_005_node_0.tif 1.78GB
images/patient_005_node_1.tif 1.45GB
images/patient_005_node_2.tif 3.53GB
images/patient_005_node_3.tif 3.27GB
images/patient_005_node_4.tif 2.94GB
images/patient_006_node_0.tif 2.81GB
images/patient_006_node_1.tif 4.13GB
images/patient_006_node_2.tif 1.65GB
images/patient_006_node_3.tif 2.38GB
images/patient_006_node_4.tif 2.41GB
images/patient_007_node_0.tif 2.71GB
images/patient_007_node_1.tif 2.21GB
images/patient_007_node_2.tif 2.16GB
images/patient_007_node_3.tif 1.34GB
images/patient_007_node_4.tif 3.36GB
images/patient_008_node_0.tif 2.40GB
images/patient_008_node_1.tif 2.35GB
images/patient_008_node_2.tif 2.07GB
images/patient_008_node_3.tif 2.10GB
images/patient_008_node_4.tif 2.20GB
images/patient_009_node_0.tif 3.94GB
images/patient_009_node_1.tif 3.35GB
images/patient_009_node_2.tif 1.51GB
images/patient_009_node_3.tif 4.05GB
images/patient_009_node_4.tif 1.95GB
images/patient_010_node_0.tif 2.04GB
images/patient_010_node_1.tif 2.10GB
images/patient_010_node_2.tif 3.62GB
images/patient_010_node_3.tif 2.44GB
images/patient_010_node_4.tif 2.29GB
images/patient_011_node_0.tif 2.42GB
images/patient_011_node_1.tif 2.69GB
images/patient_011_node_2.tif 3.36GB
images/patient_011_node_3.tif 3.00GB
images/patient_011_node_4.tif 2.56GB
images/patient_012_node_0.tif 1.97GB
images/patient_012_node_1.tif 2.63GB
images/patient_012_node_2.tif 2.21GB
images/patient_012_node_3.tif 2.01GB
images/patient_012_node_4.tif 2.67GB
images/patient_013_node_0.tif 4.06GB
images/patient_013_node_1.tif 2.20GB
images/patient_013_node_2.tif 3.39GB
images/patient_013_node_3.tif 3.57GB
images/patient_013_node_4.tif 2.84GB
images/patient_014_node_0.tif 2.61GB
images/patient_014_node_1.tif 2.17GB
images/patient_014_node_2.tif 2.61GB
images/patient_014_node_3.tif 1.44GB
images/patient_014_node_4.tif 4.13GB
images/patient_015_node_0.tif 2.11GB
images/patient_015_node_1.tif 2.17GB
images/patient_015_node_2.tif 2.36GB
images/patient_015_node_3.tif 2.11GB
images/patient_015_node_4.tif 1.99GB
images/patient_016_node_0.tif 3.70GB
images/patient_016_node_1.tif 2.81GB
images/patient_016_node_2.tif 2.01GB
images/patient_016_node_3.tif 2.77GB
images/patient_016_node_4.tif 2.37GB
images/patient_017_node_0.tif 1.80GB
images/patient_017_node_1.tif 2.40GB
images/patient_017_node_2.tif 3.86GB
images/patient_017_node_3.tif 2.26GB
images/patient_017_node_4.tif 2.82GB
images/patient_018_node_0.tif 2.29GB
images/patient_018_node_1.tif 4.52GB
images/patient_018_node_2.tif 3.23GB
images/patient_018_node_3.tif 2.62GB
images/patient_018_node_4.tif 2.83GB
images/patient_019_node_0.tif 4.53GB
images/patient_019_node_1.tif 2.42GB
images/patient_019_node_2.tif 3.64GB
images/patient_019_node_3.tif 2.98GB
images/patient_019_node_4.tif 1.26GB
images/patient_020_node_0.tif 4.46GB
images/patient_020_node_1.tif 3.88GB
images/patient_020_node_2.tif 6.03GB
images/patient_020_node_3.tif 4.79GB
images/patient_020_node_4.tif 5.98GB
images/patient_021_node_0.tif 5.64GB
images/patient_021_node_1.tif 4.44GB
images/patient_021_node_2.tif 3.99GB
images/patient_021_node_3.tif 4.34GB
images/patient_021_node_4.tif 4.43GB
images/patient_022_node_0.tif 4.09GB
images/patient_022_node_1.tif 7.62GB
images/patient_022_node_2.tif 5.87GB
images/patient_022_node_3.tif 3.64GB
images/patient_022_node_4.tif 4.30GB
images/patient_023_node_0.tif 3.70GB
images/patient_023_node_1.tif 2.33GB
images/patient_023_node_2.tif 4.78GB
images/patient_023_node_3.tif 4.76GB
images/patient_023_node_4.tif 3.03GB
images/patient_024_node_0.tif 6.84GB
images/patient_024_node_1.tif 3.25GB
images/patient_024_node_2.tif 7.11GB
images/patient_024_node_3.tif 4.75GB
images/patient_024_node_4.tif 6.24GB
images/patient_025_node_0.tif 5.69GB
images/patient_025_node_1.tif 3.11GB
images/patient_025_node_2.tif 4.03GB
images/patient_025_node_3.tif 2.89GB
images/patient_025_node_4.tif 4.61GB
images/patient_026_node_0.tif 2.82GB
images/patient_026_node_1.tif 4.60GB
images/patient_026_node_2.tif 3.35GB
images/patient_026_node_3.tif 6.58GB
images/patient_026_node_4.tif 4.38GB
images/patient_027_node_0.tif 5.74GB
images/patient_027_node_1.tif 4.38GB
images/patient_027_node_2.tif 5.17GB
images/patient_027_node_3.tif 3.96GB
images/patient_027_node_4.tif 3.20GB
images/patient_028_node_0.tif 4.89GB
images/patient_028_node_1.tif 5.83GB
images/patient_028_node_2.tif 2.78GB
images/patient_028_node_3.tif 5.12GB
images/patient_028_node_4.tif 4.73GB
images/patient_029_node_0.tif 5.50GB
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Type: Dataset
Tags:whole-slide image, pathology, histology

Bibtex:
@article{,
title= {CAMELYON17 dataset},
journal= {},
author= {Peter Bandi},
year= {},
url= {https://camelyon17.grand-challenge.org/data},
abstract= {CAMELYON17 challenge dataset. 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. The dataset contains 1000 WSIs of 200 artificial patients from 5 different medical center and exhaustive annotations for 10 WSIs from each center. The dataset is a slightly updated version of the one available on GigaScience at https://doi.org/10.1093/gigascience/giy065. The changes are: 1. Generated mask files were added for each annotated WSI and 50 additional WSI without tumor with value 1 for normal tissue, and 2 for tumor areas in the corresponding WSI. 2. The images are shared without zipping them together per patient.},
keywords= {whole-slide image, pathology, histology},
terms= {},
license= {https://creativecommons.org/publicdomain/zero/1.0},
superseded= {}
}


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