Machine Learning-driven Histotype Diagnosis of Ovarian Carcinoma: Insights from the OCEAN AI Challenge
Bibliographic record
Abstract
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 AI’s 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".