The goal of the UBC Ovarian Cancer subtypE clAssification and outlier detectioN (UBC-OCEAN) competition is to classify ovarian cancer subtypes. You will build a model trained on the world's most extensive ovarian cancer dataset of histopathology images obtained from more than 20 medical centers.

Your work will help enhance the applicability and accessibility of accurate ovarian cancer diagnoses.

This competition is hosted by the **[Artificial Intelligence in Medicine (AIM) Lab](https://aimlab.ca)** at the University of British Columbia. Established in 2019 under the guidance of [Dr. Ali Bashashati](https://aimlab.ca/team/), the AIM Lab specializes in the convergence of computational science, engineering, and biomedicine. Our primary goal is to advance the development of machine learning algorithms and software solutions that integrate various omics and imaging data sources. We are dedicated to uncovering new biological insights and identifying biomarkers that can enhance treatment selection strategies for cancer.