SWMA-UNet: Multi-Path Attention Network for Improved Medical Image Segmentation

Xianfang Tang, Jincan Li, Qianrui Liu, Chang Zhou, Pan Zeng, Yajie Meng, Junlin Xu, Geng Tian, Jialiang Yang

Abstract: In recent years, deep learning achieves significant advancements in medical image segmentation. Research finds that integrating Transformers and CNNs effectively addresses the limitations of CNNs in managing long-distance dependencies and understanding global information.However, existing models typically employ a serial approach to combine Transformers and CNNs, which complicates the simultaneous processing of global and local information. To address this, our study proposes a parallel multi-path attention architecture, SWMA-UNET, that integrates Transformers and CNNs. This architecture deeply mines features through parallel strategies while capturing both local details and global context information, thereby enhancing the accuracy of medical image segmentation. Experimental results indicate that our method surpasses all previously reported methods in the literature on the Synapse, ACDC, ISIC 2018 and MoNuSeg datasets.

External IDs:dblp:journals/titb/TangLLZZMXTY25