Bivariate copula modeling reveals that higher radiologist-AI correlation for diseased cases boosts AUC of rule-out ROC curves, with opposite effects for rule-in, consistent with mammography data.
AI-based strategies to reduce workload in breast cancer screening with mammography and tomosynthesis: A retrospective evaluation
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A theory of ROC analysis of rule-out and rule-in diagnostics with applications to mammography data
Bivariate copula modeling reveals that higher radiologist-AI correlation for diseased cases boosts AUC of rule-out ROC curves, with opposite effects for rule-in, consistent with mammography data.