DyGLNet: Hybrid global-local feature fusion with dynamic upsampling for medical image segmentation
文献类型：期刊论文
| 作者 | Zhao, Yican2; Wang, Ce1; Hao, You3; Li, Lei2; Liao, Tianli2 |
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2026-05-01 |
| 卷号 | 173页码:12 |
| 关键词 | Medical image segmentation Feature fusion Dynamic upsampling Multi-scale |
| ISSN号 | 0031-3203 |
| DOI | 10.1016/j.patcog.2025.112792 |
| 英文摘要 | Medical image segmentation grapples with challenges including multi-scale lesion variability, ill-defined tissue boundaries, and computationally intensive processing demands. This paper proposes the DyGLNet, which achieves efficient and accurate segmentation by fusing global and local features with a dynamic upsampling mechanism. The model innovatively designs a hybrid feature extraction module (SHDCBlock), combining single-head self-attention and multi-scale dilated convolutions to model local details and global context collaboratively. We further introduce a lightweight dynamic adaptive upsampling module (DyFusionUp) to realize highfidelity reconstruction of feature maps based on learnable offsets and reduce computational overhead. Experiments on seven public datasets demonstrate that DyGLNet outperforms existing methods, particularly excelling in boundary accuracy and small-object segmentation. Meanwhile, it exhibits lower computation complexity, enabling an efficient and reliable solution for clinical medical image analysis. The code is available at https://github.com/YeeCan-Zhao/DyGLNet. |
| 资助项目 | Natural Science Foundation of Henan Province[222300420140] ; National Natural Science Foundation of China[62301532] ; National Natural Science Foundation of China[62303438] ; Science and Technology Research Project of Henan Provincial Department of Science and Technology[242103810066] ; Natural Science Foundation of Jiangsu Province[BK20230282] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001634957300001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42927]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Liao, Tianli |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Sci, Shenzhen 518107, Guangdong, Peoples R China 2.Henan Univ Technol, Coll Informat Sci & Engn, Zhengzhou 450001, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhao, Yican,Wang, Ce,Hao, You,et al. DyGLNet: Hybrid global-local feature fusion with dynamic upsampling for medical image segmentation[J]. PATTERN RECOGNITION,2026,173:12. |
| APA | Zhao, Yican,Wang, Ce,Hao, You,Li, Lei,&Liao, Tianli.(2026).DyGLNet: Hybrid global-local feature fusion with dynamic upsampling for medical image segmentation.PATTERN RECOGNITION,173,12. |
| MLA | Zhao, Yican,et al."DyGLNet: Hybrid global-local feature fusion with dynamic upsampling for medical image segmentation".PATTERN RECOGNITION 173(2026):12. |
入库方式： OAI收割
来源：计算技术研究所
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