文献类型：期刊文献

英文题名：SEMU-Net: A Boundary Semantic-Aware Network for Medical Image Segmentation

作者：Jiang, Zongying[1]; Dai, Li[1]; Jin, Yuxiong[1]; Li, Jianhua[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China

年份：2025

外文期刊名：Proceedings of the International Joint Conference on Neural Networks

收录：EI(收录号：20255019675134)

语种：英文

摘要：Due to the limitations of medical imaging devices, existing segmentation models struggle to find boundary pixels that strictly distinguish the background from regions of interest, where boundary pixels are critical for accurate segmentation. This problem is described as semantic ambiguity arising from blurred boundaries. To address this issue, we propose a novel U-Net architecture with a Semantic Enhancement Module (SEMU-Net). This network mainly utilizes the Latent Diffusion Model (LDM) to generate additional semantic features by exploiting LDM's ability to compress and comprehend semantic information effectively. The Semantic Enhancement Module (SEM) comprises two components: the Dual-Cross Attention Module and the Token Pruning Module. In the Dual-Cross Attention Module, semantic features are integrated into a U-Net-like framework, enabling the network to incorporate semantic information from both spatial and channel dimensions selectively. The Token Pruning technique compresses the large number of tokens generated during the tokenization process into a smaller subset of significant tokens. With the aid of these two modules, the fusion process is optimized and computational costs are minimized. Experimental results on two public datasets demonstrate that the proposed SEMU-Net has superior performance over other state-of-the-art algorithms. ? 2025 IEEE.