Northeast Forestry University

The work proposes KANResDiff, which learns local residual diffusion via Kolmogorov-Arnold Networks for ambiguous medical image segmentation: it replaces MLP linear time embeddings with B-spline-based Independent Time Encoding to strengthen independence across inference stages, and injects a deterministic residual prior with learnable weights through a Residual Schrödinger Bridge, achieving state-of-the-art GED and HM-IoU on the two public datasets LIDC and ISIC3 with maximum improvements of 16.8% and 7.7% respectively while keeping competitive MDM performance.

The work proposes KANResDiff, which learns local residual diffusion via Kolmogorov-Arnold Networks for ambiguous medical image segmentation: it replaces MLP linear time embeddings with B-spline-based Independent Time Encoding to strengthen independence across inference stages, and injects a deterministic residual prior with learnable weights through a Residual Schrödinger Bridge, achieving state-of-the-art GED and HM-IoU on the two public datasets LIDC and ISIC3 with maximum improvements of 16.8% and 7.7% respectively while keeping competitive MDM performance.