Scholarlink

<p>In the field of medical image segmentation, techniques based on convolutional neural networks and Transformers have been extensively developed. However, the current mainstream methods for medical image segmentation are mainly based on standard convolutions or depthwise separable convolutions, which essentially process on fixed and ordered feature channels. This inherent constraint limits the model’s ability to learn cross-channel contextual relationships. To address these challenges, we introduce a novel architecture strategy with channel shuffle as the core operation and the Multi-Scale Dynamic Channel Shuffle Attention (MS-DCSA) mechanism. Our proposed mechanism actively disrupts the fixed order of channels to achieve more effective cross-channel information exchange, promoting the model to learn richer global-local feature representations. Experiments on the Synapse, ACDC, CVC-ClinicDB, and ISIC-2017 datasets have shown that MS-DCSNet achieves excellent segmentation accuracy in various types of medical image segmentation with average Dice scores of 84.22%, 92.48%, 88.70%, and 96.45%. These results not only significantly surpass most existing methods, but also demonstrate the powerful segmentation ability and excellent generalization performance of MS-DCSNet.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MS-DCSNet: Global-local feature interaction and multi-scale dynamic channel shuffle attention for medical image segmentation

  • 期刊论文

  • 发表日期：

    2025年12月20日

查看全文
  • Hao Zhai,
  • Yang Zhang,
  • Lei Yu,
  • Ping Yu,
  • Yuanzhe Zhang

摘要

In the field of medical image segmentation, techniques based on convolutional neural networks and Transformers have been extensively developed. However, the current mainstream methods for medical image segmentation are mainly based on standard convolutions or depthwise separable convolutions, which essentially process on fixed and ordered feature channels. This inherent constraint limits the model’s ability to learn cross-channel contextual relationships. To address these challenges, we introduce a novel architecture strategy with channel shuffle as the core operation and the Multi-Scale Dynamic Channel Shuffle Attention (MS-DCSA) mechanism. Our proposed mechanism actively disrupts the fixed order of channels to achieve more effective cross-channel information exchange, promoting the model to learn richer global-local feature representations. Experiments on the Synapse, ACDC, CVC-ClinicDB, and ISIC-2017 datasets have shown that MS-DCSNet achieves excellent segmentation accuracy in various types of medical image segmentation with average Dice scores of 84.22%, 92.48%, 88.70%, and 96.45%. These results not only significantly surpass most existing methods, but also demonstrate the powerful segmentation ability and excellent generalization performance of MS-DCSNet.