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University of Oxford
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Explicitly modeling annotator bias and variability can significantly enhance uncertainty calibration in medical image segmentation without sacrificing accuracy.
Stop relying on a single "best" model: ToolSelect adaptively picks the right specialist model for each healthcare query, outperforming SOTA methods by learning from model behavior.
