New methods CM-ATC and CM-DoC estimate a classifier's confusion matrix on unlabeled medical images by applying class-specific confidence thresholds and offsets, outperforming the prior baseline on real-world shifts but failing under prevalence shifts.
npj Digital Medicine5(1), 1–8 (Apr 2022)
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Label-free estimation of clinically relevant performance metrics under distribution shifts
New methods CM-ATC and CM-DoC estimate a classifier's confusion matrix on unlabeled medical images by applying class-specific confidence thresholds and offsets, outperforming the prior baseline on real-world shifts but failing under prevalence shifts.