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This paper introduces Structure-Guided Dynamic Convolution (SGDC), a novel dynamic convolution mechanism for medical image segmentation that addresses the over-smoothing issues caused by average pooling in existing methods. SGDC uses an explicitly supervised structure-extraction branch to guide the generation of dynamic kernels and gating signals, enabling structure-aware feature modulation. Experiments on multiple medical image datasets (ISIC 2016, PH2, ISIC 2018, and CoNIC) demonstrate that SGDC achieves state-of-the-art performance with improved boundary fidelity and IoU gains compared to pooling-based baselines.
Ditch average pooling: SGDC uses explicit structural guidance to generate dynamic convolution kernels, boosting medical image segmentation accuracy and boundary fidelity.
Spatially variant dynamic convolution provides a principled approach of integrating spatial adaptivity into deep neural networks. However, mainstream designs in medical segmentation commonly generate dynamic kernels through average pooling, which implicitly collapses high-frequency spatial details into a coarse, spatially-compressed representation, leading to over-smoothed predictions that degrade the fidelity of fine-grained clinical structures. To address this limitation, we propose a novel Structure-Guided Dynamic Convolution (SGDC) mechanism, which leverages an explicitly supervised structure-extraction branch to guide the generation of dynamic kernels and gating signals for structure-aware feature modulation. Specifically, the high-fidelity boundary information from this auxiliary branch is fused with semantic features to enable spatially-precise feature modulation. By replacing context aggregation with pixel-wise structural guidance, the proposed design effectively prevents the information loss introduced by average pooling. Experimental results show that SGDC achieves state-of-the-art performance on ISIC 2016, PH2, ISIC 2018, and CoNIC datasets, delivering superior boundary fidelity by reducing the Hausdorff Distance (HD95) by 2.05, and providing consistent IoU gains of 0.99\%-1.49\% over pooling-based baselines. Moreover, the mechanism exhibits strong potential for extension to other fine-grained, structure-sensitive vision tasks, such as small-object detection, offering a principled solution for preserving structural integrity in medical image analysis. To facilitate reproducibility and encourage further research, the implementation code for both our SGE and SGDC modules has been is publicly released at https://github.com/solstice0621/SGDC.
