---
dataset_info:
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  - name: image
    dtype: image
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 40279487.0
    num_examples: 513
  download_size: 40287929
  dataset_size: 40279487.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# [UBC-OCEAN](https://www.kaggle.com/competitions/UBC-OCEAN/overview)
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}
}
```