[1]MENG Xiangfu,YANG Yuzhuo,ZHANG Xiaoyan,et al.Medical image segmentation network based on group attention parallel encoding[J].CAAI transactions on intelligent systems,2026,21(5):1194-1210.[doi:10.11992/tis.202511010]
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Medical image segmentation network based on group attention parallel encoding

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1194-1210 Column: 学术论文—机器学习 Public date: 2026-09-05
Title:
Medical image segmentation network based on group attention parallel encoding
Author(s):
MENG Xiangfu1;  YANG Yuzhuo1;  ZHANG Xiaoyan1;  LI Shuai2
1. School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China;
2. Liaoning Health Industry Group Fuxin Mine General Hospital, Fuxin 123000, China
Keywords:
medical image segmentation; dual-branch encoder; group attention; feature extraction; Transformer; CNN; parallel fusion; spatial-channel decoding
CLC:
TP391.4
DOI:
10.11992/tis.202511010
Abstract:
For the task of medical image segmentation, traditional single-branch convolutional neural network (CNN) architectures, limited by their receptive fields, struggle to effectively integrate local details with global semantics. This results in insufficient modeling of multi-scale structures and weak generalization across modalities. To address these limitations, we propose a novel segmentation network with group attention parallel fusion encoding. It employs a parallel dual-branch encoder combining Transformer and CNN to extract global semantic information and local details from images, respectively. The group attention parallel fusion module following the encoder resolves the disconnection between local and global semantics as well as conflicts in multi-scale structures. Additionally, the spatial-channel dual-attention gating module in the decoder effectively tackles feature interference in medical image segmentation, thereby enhancing segmentation accuracy. Extensive experiments were conducted on the Synapse, ACDC and AVT datasets, yielding dice similarity coefficient (DSC) of 84.86%, 91.66%, and 88.79%, and 95% hausdorff distance (HD95) of 13.54, 1.20, and 4.02 mm, respectively. The results demonstrate that compared with existing major medical image segmentation models, our approach achieves higher segmentation accuracy and robustness, especially when dealing with complex anatomical structures of organ data.
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