Improving Calibration of Black-Box Radiology AI Using Test-Time Augmentation
Radiology AI systems increasingly inform clinical decisions such as triage, follow-up imaging, and treatment planning. For these decisions to be made safely, model outputs must be well calibrated, meaning predicted probabilities accurately reflect true risk. Many standard techniques for improving calibration, such as M...
Nathan Le, Magdalini Paschali, Arogya Koirala et al.
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