ROLE-PRESERVING MULTISCALE EVIDENCE LEARNING FOR COMPACT MEDICAL IMAGE SEGMENTATION

Compact medical image segmentation requires object-level discrimination without sacrificing low-contrast or irregular boundaries. Many compact encoder-decoder networks merge coarse semantics, regional structure, and fine texture into a single stream; an erroneous coarse response can therefore become a coherent false-positive object, whereas strong filtering can remove a genuine target. We propose a role-preserving multiscale evidence framework that assigns object evidence construction, regional continuity, boundary reconstruction, and class readout to explicit paths. At H/16, local and regional observations are related before a compact KAN mapper modulates a stable high-dimensional convolutional carrier. At higher resolutions, semantic, regional, and detail representations retain separate carriers and interact through geometric correspondence during transport. A cross-scale prototype head then allocates dense evidence to image-conditioned foreground and background prototypes instead of drawing an independent mask. Under a common three-seed protocol on three public benchmarks spanning ultrasound, endoscopy, and histopathology, our 5.79M-parameter model obtains mean IoUs of 73.97%, 85.06%, and 82.78% on BUSI, CVC, and GlaS, respectively. It achieves the highest BUSI and CVC mean IoU among the evaluated methods and improves the three-dataset average IoU from 79.52% for U-KAN to 80.60%. These results support role-explicit evidence transport as an effective architectural choice under the evaluated compact-segmentation protocol.