CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification

Summary

CalCErt is a post-hoc method that certifies bin-wise confidence calibration for any pretrained differentiable classifier under adversarial perturbation. It combines empirical calibration estimates, statistical concentration bounds, and local Lipschitz estimates of the confidence function to derive upper bounds on worst-case miscalibration within an ℓ2-ball of radius R. The authors evaluate it on 11 medical image classification tasks and report substantially higher certified coverage than baseline strategies.

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