AI RESEARCH
Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation
arXiv CS.CV
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ArXi:2605.09600v1 Announce Type: cross Accurate skin lesion segmentation is vital for dermoscopic Computer-Aided Diagnosis. However, visual ambiguity and morphological irregularity often defeat spatial modeling, necessitating multi-domain architectures. Existing paradigms frequently overlook the active use of prediction uncertainty, leading to deterministic frameworks that suffer from blind cross-domain fusion and overfit to label noise. To address these issues, we propose the Uncertainty-Guided Dual-Domain Network (UGDD-Net