UGDD-Net uses uncertainty to guide bi-directional feature fusion, graph-based refinement, and adaptive loss to achieve state-of-the-art skin lesion segmentation on hard samples while producing uncertainty maps that align with expert variability.
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Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation
UGDD-Net uses uncertainty to guide bi-directional feature fusion, graph-based refinement, and adaptive loss to achieve state-of-the-art skin lesion segmentation on hard samples while producing uncertainty maps that align with expert variability.