Advances in attention mechanisms for medical image segmentation

被引：0
作者：
Zhang, Jianpeng [1 ]
Chen, Xiaomin [4 ]
Yang, Bing [2 ]
Guan, Qingbiao [2 ]
Chen, Qi [3 ]
Chen, Jian [4 ]
Wu, Qi [3 ]
Xie, Yutong [3 ]
Xia, Yong [2 ]
机构：
[1] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou, Peoples R China
[2] Northwestern Polytech Univ, Sch Comp Sci & Engn, Natl Engn Lab Integrated Aero Space Ground Ocean B, Xian, Peoples R China
[3] Univ Adelaide, Adelaide, Australia
[4] South China Univ Technol, Guangzhou, Peoples R China
来源：
COMPUTER SCIENCE REVIEW | 2025年 / 56卷
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Medical image segmentation; Attention mechanism; Transformer; Mamba; Deep learning; CONVOLUTIONAL NEURAL-NETWORK; SWIN TRANSFORMER; MULTIORGAN; FIELD;
D O I：
10.1016/j.cosrev.2024.100721
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed about 200 articles related to medical image segmentation, and divided them into three groups based on their attention mechanisms, Pre-Transformer attention, Transformer attention and Mamba-related attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional, Transformer and Mamba attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios. Finally, we maintain the paper list and open-source code at here.
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页数：18
相关论文
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