用可学习的局部残差扩散模型提升医学图像分割的多样性与准确性
KANResDiff: Learning Local Residual Diffusion via Kolmogorov-Arnold Network for Ambiguous Medical Image Segmentation
- 基于柯尔莫哥洛夫-阿诺德网络构建可学习的局部残差扩散机制
- 在两个公开数据集上实现16.8%和7.7%的指标提升，超越现有方法
- 适合需要生成多合理分割结果的医学影像分析场景
模糊医学图像分割旨在生成一系列多样且合理的分割假设。然而，现有方法以固定预设方式引入随机性，无法形成渐进式语义建模过程。为此，我们提出KANResDiff，通过柯尔莫哥洛夫-阿诺德网络学习局部残差扩散，为不同阶段赋予差异化角色。具体而言，提出独立时间编码，使用样条基时间嵌入替代MLP的线性嵌入，增强推理阶段间的独立性，并为各阶段分配渐进语义角色。提出残差薛定谔桥，通过构建局部薛定谔桥注入可学习权重的确定性残差先验，实现灵活的确定性-随机性交互与阶段感知的模糊建模。在两个公开数据集上的大量实验表明，KANResDiff在GED和HM-IoU上达到当前最优性能，最大提升分别为16.8%和7.7%，同时在MDM指标上保持竞争力。源代码已开源。
原文摘要 · Abstract (English)
Ambiguous medical image segmentation aims to provide a series of diverse but plausible segmentation hypotheses. However, existing methods introduce stochasticity in a fixed and pre-defined manner, failing to form a progressive semantic modeling process. To address these challenges, we propose KANResDiff to learn local residual diffusion with Kolmogorov-Arnold Network, thereby assigning distinct roles across stages for ambiguity modeling. Specifically, we propose Independent Time Encoding that offers spline-based time embeddings instead of linear ones from MLPs, which enhances the independence across inference stages and assigns progressive semantic roles to different stages. We propose Residual Schrodinger Bridge that injects deterministic residual prior with learnable weights by constructing local Schrodinger Bridge instead of following manually settings, achieving a flexible deterministic-stochastic interaction and stage-aware ambiguity modeling thanks to local optimal diffusion path. Extensive experimental results on two public datasets demonstrate that KANResDiff achieves SOTA performance on GED and HM-IoU, with maximum improvements of 16.8% and 7.7%, respectively, while maintaining competitive performance on the MDM metric. Source code is available at https://github.com/PerceptionComputingLab/KANResDiff.
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