用热传导模拟增强医学图像分割的全局上下文建模能力
Enhancing Medical Image Segmentation via Heat Conduction Equation
- 结合状态空间模块与热传导算子，实现高效长程依赖推理
- 在腹部CT数据集上取得0.8719的最高Dice分数
- 适合需要高精度分割且计算资源受限的医疗场景
医学图像分割模型在实际计算预算下难以实现高效的全局上下文建模和长距离依赖推理。本文提出一种混合架构，采用U-Mamba结合热传导方程，在瓶颈层引入热传导算子（HCOs），模拟频域热扩散以增强语义抽象。实验结果表明，该模型在腹部CT数据集上达到最高的DSC（0.8719）。研究显示，将状态空间动态与基于热的全局扩散相结合，为医学分割任务提供了一种可扩展的解决方案。
原文摘要 · Abstract (English)
Medical image segmentation models struggle to achieve efficient global context modeling and long-range dependency reasoning under practical computational budgets. In this work, we propose a hybrid architecture utilizing U-Mamba with Heat Conduction Equation, which combines state-space modules for efficient long-range reasoning with Heat Conduction Operators (HCOs) in the bottleneck layers, simulating frequency-domain thermal diffusion for enhanced semantic abstraction. Experimental results show that our model attains the highest DSC (0.8719) on the Abdomen CT dataset. It suggests that blending state-space dynamics with heat-based global diffusion offers a scalable solution for medical segmentation tasks.
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