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arXiv Computer Vision By Gb\`egninougbo Aurel Davy Tchokponhoue, Sevda \"O\u{g}\"ut, Ali Idri, Dorina Thanou, Pascal Frossard

Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning

Read the original on arXiv Computer Vision →

The paper introduces DICE, a plug‑and‑play framework that ensembles multiple frozen pathology foundation models (PFMs) and estimates uncertainty from their consensus. By aligning ensemble members through deep mutual learning, DICE theoretically bounds epistemic uncertainty and localizes abnormalities at the patch level without explicit supervision. Experiments on three whole‑slide image benchmarks show that DICE provides reliable uncertainty estimates, accurately flags failure‑prone cases in both in‑ and out‑of‑distribution settings, and matches or surpasses state‑of‑the‑art baselines in classification, calibration, and localization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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