AI RESEARCH
Physics-Grounded Adversarial Stain Augmentation with Calibrated Coverage Guarantees
arXiv CS.LG
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ArXi:2605.13889v1 Announce Type: cross Stain variation across hospitals degrades histopathology models at deployment. Existing augmentation methods perturb color spaces with arbitrary hyperparameters, lacking both a principled budget and coverage guarantees for unseen centers. We propose \textbf{C}alibrated \textbf{A}dversarial \textbf{S}tain \textbf{A}ugmentation (\textbf{CASA}), which performs adversarial augmentation in the Macenko stain parameter space with a budget calibrated from multi-center statistics via the DKW inequality.