Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
DICE is a plug-and-play framework that combines multiple frozen pathology foundation models (PFMs) into an ensemble to estimate uncertainty. It aligns these experts through deep mutual learning and representation alignment, using their disagreement as a proxy for uncertainty. This allows the system to provide reliable confidence estimates for whole-slide image analysis.