AI RESEARCH
Adaptive Conformal Prediction for Reliable and Explainable Medical Image Classification
arXiv CS.LG
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ArXi:2605.12917v1 Announce Type: cross Deep learning models for medical imaging often exhibit overconfidence, creating safety risks in ambiguous diagnostic scenarios. While Conformal Prediction (CP) provides distribution-free statistical guarantees, standard methods such as Regularized Adaptive Prediction Sets (RAPS) optimize for average efficiency and can mask severe failures on difficult inputs. We propose an Adaptive Lambda Criterion for RAPS that minimizes the worst-case coverage violation across prediction set size strata.