metadata

license: cc0-1.0
size_categories:
  - 100K<n<1M
task_categories:
  - image-classification
pretty_name: ' PatchCamelyon (PCam)'
tags:
  - medical
  - biology
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': '0'
            '1': '1'
  splits:
    - name: train
      num_bytes: 1152417815.736
      num_examples: 262144
    - name: validation
      num_bytes: 146182832.56
      num_examples: 32768
    - name: test
      num_bytes: 141718983.176
      num_examples: 32768
  download_size: 1386391969
  dataset_size: 1440319631.4720001
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

PatchCamelyon (PCam)

This is a reupload of the PatchCamelyon (PCam) dataset in jpeg/csv format instead of H5 files. The original can be found on Github.

If you use this dataset, please cite the original paper:

@inproceedings{veeling2018rotation,
  title={Rotation Equivariant CNNs for Digital Pathology},
  author={Veeling, Bastiaan S and Linmans, Jasper and Winkens, Jim and Cohen, Taco and Welling, Max},
  booktitle={Medical Image Computing and Computer Assisted Intervention--MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11},
  pages={210--218},
  year={2018},
  organization={Springer}
}