DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic Upsampling for Medical Image Segmentation

• URL: http://arxiv.org/abs/2509.12763v1
• Date: Tue, 16 Sep 2025 07:24:20 GMT
• Title: DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic Upsampling for Medical Image Segmentation
• Authors: Yican Zhao, Ce Wang, You Hao, Lei Li, Tianli Liao,
• Abstract summary: DyGLNet achieves efficient and accurate segmentation by fusing global and local features with a dynamic upsampling mechanism.<n>Experiments on seven public datasets demonstrate that DyGLNet outperforms existing methods.<n>DyGLNet exhibits lower complexity, enabling an efficient and reliable solution for clinical medical image analysis.
• Score: 8.283216541594284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 dynamic adaptive upsampling module (DyFusionUp) to realize high-fidelity reconstruction of feature maps based on learnable offsets. Then, a lightweight design is adopted to 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 will be made available soon.

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