MSA-SAM2Net: A Polyp Segmentation Framework Based on Large Kernel Multi-Scale Attention

Polyp segmentation plays a critical role in the diagnosis of colorectal cancer but remains challenging due to the diverse morphologies and indistinct boundaries of polyps. These challenges are particularly prominent in small and irregularly shaped polyps, where precise edge detail capture is essential. While the Segment Anything Model 2 (SAM2) has demonstrated remarkable progress in medical image segmentation through its strong pre-training capabilities, its reliance on global feature extraction strategies limits its ability to model local details and fine-grained features, particularly for complex edges. To address these shortcomings, we propose MSA-SAM2Net, an enhanced framework that integrates a Large Kernel Size Attention (LKSA) module and an Edge-Boosting Module (EBM) to improve feature extraction and edge detail perception. Specifically, LKSA employs multi-scale and gating mechanisms to capture both global context and local details, producing richer attention maps. Meanwhile, EBM enhances segmentation accuracy by focusing on edge regions. Additionally, we introduce a Large-Kernel Grouped Attention (LGA) to further refine feature selection and fusion. Comprehensive experiments on five publicly available and challenging polyp segmentation datasets demonstrate the effectiveness of MSA-SAM2Net, achieving state-of-the-art performance on all five datasets. The source code will be available at: https://github.com/coderMelon0216/MSA-SAM2Net