README.md

metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: string
  splits:
    - name: train
      num_bytes: 40279487
      num_examples: 513
  download_size: 40287929
  dataset_size: 40279487
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

UBC-OCEAN

UBC Ovarian Cancer Subtype Classification and Outlier Detection [UBC-OCEAN] is the world's most extensive ovarian cancer dataset of histopathology images obtained from more than 20 medical centers.

Navigating Ovarian Cancer: Unveiling Common Histotypes and Unearthing Rare Variants

Citation

@misc{UBC-OCEAN,
    author = {Ali Bashashati, Hossein Farahani, OTTA Consortium, Anthony Karnezis, Ardalan Akbari, Sirim Kim, Ashley Chow, Sohier Dane, Allen Zhang, Maryam Asadi},
    title = {UBC Ovarian Cancer Subtype Classification and Outlier Detection (UBC-OCEAN)},
    publisher = {Kaggle},
    year = {2023},
    url = {https://kaggle.com/competitions/UBC-OCEAN}
}