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About this paper
Gbègninougbo Aurel Davy Tchokponhoue, Sevda Öğüt, Ali Idri, Dorina Thanou, Pascal Frossard
DICE addresses uncertainty in whole-slide pathology by training slide-level heads from five frozen pathology models to predict diagnoses and learn from one another. It averages their probabilities, using predictive entropy to rank cases for review. On PANDA, DICE achieves F1 of 76.6 ± 1.02% versus 73.6 ± 1.02% with early fusion, and deferral reduces retained error; this supports retrospective triage, not a tested clinical workflow.
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