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arXiv · 2511.03260

Enhancing Medical Image Segmentation via Heat Conduction Equation

Rong Wu·Yim-Sang Yu
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Abstract

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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Rong Wu, Yim-Sang Yu. 2025-11-05. Enhancing Medical Image Segmentation via Heat Conduction Equation. https://arxiv.org/abs/2511.03260

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