OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
author Forbes, F
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A survey of 136 U.S. clinicians finds that autonomous AI prescribing would require confidence-based escalation, differentiated uncertainty communication, and inferential transparency to gain acceptance and properly allocate liability.
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OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
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The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
A survey of 136 U.S. clinicians finds that autonomous AI prescribing would require confidence-based escalation, differentiated uncertainty communication, and inferential transparency to gain acceptance and properly allocate liability.