CAMELYON16 dataset
Peter Bandi

CAMELYON16 (964 files) 3.64kB
masks/tumor_111_mask.tif 30.27MB
masks/tumor_109_mask.tif 32.87MB
masks/tumor_110_mask.tif 8.39MB
masks/tumor_108_mask.tif 37.10MB
masks/tumor_106_mask.tif 40.04MB
masks/tumor_107_mask.tif 21.16MB
masks/tumor_105_mask.tif 28.25MB
masks/tumor_103_mask.tif 30.23MB
masks/tumor_104_mask.tif 37.10MB
masks/tumor_101_mask.tif 22.75MB
masks/tumor_102_mask.tif 56.77MB
masks/tumor_099_mask.tif 25.89MB
masks/tumor_100_mask.tif 34.17MB
masks/tumor_097_mask.tif 32.13MB
masks/tumor_098_mask.tif 18.16MB
masks/tumor_095_mask.tif 8.33MB
masks/tumor_096_mask.tif 22.84MB
masks/tumor_093_mask.tif 22.90MB
masks/tumor_094_mask.tif 29.14MB
masks/tumor_091_mask.tif 8.36MB
masks/tumor_092_mask.tif 2.13MB
masks/tumor_089_mask.tif 54.88MB
masks/tumor_090_mask.tif 45.05MB
masks/tumor_088_mask.tif 28.62MB
masks/tumor_086_mask.tif 45.86MB
masks/tumor_087_mask.tif 22.59MB
masks/tumor_085_mask.tif 4.90MB
masks/tumor_083_mask.tif 43.18MB
masks/tumor_084_mask.tif 13.33MB
masks/tumor_082_mask.tif 17.29MB
masks/tumor_080_mask.tif 38.70MB
masks/tumor_081_mask.tif 21.12MB
masks/tumor_079_mask.tif 1.86MB
masks/tumor_077_mask.tif 30.05MB
masks/tumor_078_mask.tif 32.43MB
masks/tumor_076_mask.tif 33.42MB
masks/tumor_074_mask.tif 31.60MB
masks/tumor_075_mask.tif 22.59MB
masks/tumor_072_mask.tif 37.85MB
masks/tumor_073_mask.tif 34.31MB
masks/tumor_070_mask.tif 22.87MB
masks/tumor_071_mask.tif 31.13MB
masks/tumor_068_mask.tif 27.67MB
masks/tumor_069_mask.tif 18.63MB
masks/tumor_066_mask.tif 38.33MB
masks/tumor_067_mask.tif 1.79MB
masks/tumor_064_mask.tif 20.38MB
masks/tumor_065_mask.tif 15.13MB
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Type: Dataset
Tags: whole-slide image, pathology, histology

title= {CAMELYON16 dataset},
journal= {},
author= {Peter Bandi},
year= {},
url= {},
abstract= {CAMELYON16 challenge dataset. The goal of CAMELYON16 challenge is to evaluate new and existing algorithms for automated detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSIs) of lymph node sections. The dataset contains 270 WSIs (159 normal slides, and 111 slides with tumor) for training, and 129 WSIs for testing. The dataset is a slightly updated version of the one available on GigaScience at The changes are: 1. The test_114.tiff WSI was exhaustively annotated. 2. Generated mask files were added for each WSI with value 1 for normal tissue, and 2 for tumor areas in the corresponding WSI.},
keywords= {whole-slide image, pathology, histology},
terms= {},
license= {},
superseded= {}

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