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.
& Cai, L.Sfma-unet: A mamba-based spatial-frequency fusion network for medical image segmentation, 1–5 (IEEE, 2025)
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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.