image
imagewidth (px)
300
300
label
stringclasses
5 values
EC
HGSC
HGSC
EC
LGSC
EC
CC
HGSC
EC
HGSC
HGSC
HGSC
CC
HGSC
EC
LGSC
HGSC
HGSC
CC
LGSC
CC
EC
HGSC
HGSC
EC
HGSC
HGSC
LGSC
CC
CC
HGSC
CC
HGSC
MC
EC
EC
MC
HGSC
EC
HGSC
EC
HGSC
HGSC
CC
HGSC
CC
CC
LGSC
EC
EC
HGSC
EC
HGSC
EC
HGSC
CC
HGSC
CC
HGSC
CC
HGSC
CC
HGSC
CC
HGSC
HGSC
EC
EC
HGSC
HGSC
CC
EC
HGSC
HGSC
HGSC
EC
EC
CC
HGSC
HGSC
HGSC
EC
HGSC
HGSC
HGSC
CC
HGSC
CC
EC
CC
HGSC
HGSC
MC
HGSC
HGSC
HGSC
EC
HGSC
EC
LGSC

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}
}
Downloads last month
31
Edit dataset card