Machine Learning-driven Histotype Diagnosis of Ovarian Carcinoma: Insights from the OCEAN AI Challenge
Asadi-Aghbolaghi, M.; Farahani, H.; Zhang, A.; Akbari, A.; Kim, S.; Chow, A.; Dane, S.; OCEAN Challenge Consortium, ; OTTA Consortium, ; G Huntsman, D.; Gilks, C. B.; Ramus, S.; Köbel, M.; N Karnezis, A.; Bashashati, A.
Show abstract
Ovarian cancer poses a significant health burden as one of the deadliest malignancies affecting women globally. Histotype assignment of epithelial ovarian cancers can be challenging due to morphologic overlap, inter-observer variability, and the lack of ancillary diagnostic techniques in some areas of the world. Moreover, rare cancers can pose particular diagnostic difficulties because of a relative lack of familiarity with them, underscoring the necessity for robust diagnostic methodologies. The emergence of Artificial Intelligence (AI) has brought promising prospects to the realm of ovarian cancer diagnosis. While various studies have underscored AIs promise, its validation across multiple healthcare centers and hospitals has been limited. Inspired by innovations in medical imaging driven by public competitions, we initiated the Ovarian Cancer subtypE clAssification and outlier detectioN (OCEAN) challenge -- the most extensive histopathology competition to date.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images 93%
- Breast invasive ductal carcinoma classification on whole slide images with weakly-supervised and transfer learning 93%
- Development of a Single Molecule Counting Assay to Differentiate Chromophobe Renal Cancer and Oncocytoma in Clinics 90%
Similar papers in this journal
- TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction 90%
- From Mutation to Prognosis: AI-HOPE-PI3K Enables Artificial Intelligence-Agent Driven Integration of PI3K Pathway Data in Colorectal Cancer Precision Medicine 90%
- Multi-run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers 90%
Similar papers in this journal
- Automated Segmentation of Hepatic Vessels and Lobules in Whole-Slide Images Using U-Net Models 90%
- BC-Predict: Mining of signal biomarkers and multilevel validation of cascade classifier for early-stage breast cancer subtyping and prognosis 89%
- Machine-learning-based determination of sex-related bladder cancer biomarkers 88%
Similar papers in this journal
- Bladder Cancer Prognosis Using Deep Neural Networks and Histopathology Images 93%
- Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope 92%
- Using an Anomaly Detection Approach for the Segmentation of Colorectal Cancer Tumors in Whole Slide Images 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.