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About this paper
Fanding Li, Chenglin Wang, Xiangyu Li, Xingyu Qiu, Xinghua Ma, Xiangming Yin, Haiyang Li, Suyu Dong and 4 more
KANResDiff models ambiguous medical image segmentation by generating multiple plausible masks while balancing structural guidance and stochastic variation across diffusion stages. It uses locally supported B-spline time encoding and a learned, timestep-dependent weight for a deterministic segmentation prior. On two datasets, it reports improved distributional matching; these results do not establish improved clinical decisions or superiority over alternative time encodings.
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