AI RESEARCH
Med-DisSeg: Dispersion-Driven Representation Learning for Fine-Grained Medical Image Segmentation
arXiv CS.CV
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ArXi:2605.14579v1 Announce Type: new Accurate medical image segmentation is fundamental to precision medicine, yet robust delineation remains challenging under heterogeneous appearances, ambiguous boundaries, and large anatomical variability. Similar intensity and texture patterns between targets and surrounding tissues often lead to blurred activations and unreliable separation. We attribute these failures to representation collapse during encoding and insufficient fine grained multi scale decoding.