通过动态上采样融合全局局部特征，提升医学图像分割精度与效率
DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic Upsampling for Medical Image Segmentation
- 设计混合特征模块，结合自注意力与多尺度空洞卷积捕捉细节与上下文
- 动态上采样模块基于可学习偏移实现高保真特征重建，边界分割更精准
- 轻量化设计降低计算开销，适合临床实时应用
医学图像分割面临多尺度病灶差异、组织边界模糊及计算负载高等挑战。本文提出DyGLNet，通过融合全局与局部特征并引入动态上采样机制，实现高效精准分割。模型创新性地设计了单头自注意力与多尺度空洞卷积结合的混合特征提取模块（SHDCBlock），协同建模局部细节与全局上下文。进一步提出动态自适应上采样模块（DyFusionUp），基于可学习偏移实现特征图的高保真重建。采用轻量化设计降低计算复杂度。在七个公开数据集上的实验表明，DyGLNet优于现有方法，尤其在边界精度和小目标分割上表现突出，同时具备更低的计算开销，为临床医学图像分析提供高效可靠的解决方案。
原文摘要 · Abstract (English)
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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