Multi-stage feature fusion network for polyp segmentation

With the rising incidence and mortality of colorectal cancer, automatic polyp segmentation has gained significant attention. To address the limitations of existing pyramid-based transformer methods in polyp segmentation, specifically their challenges with feature scale diversity and feature fusion, we propose a transformer-based multi-stage feature fusion network (MSFFNet). First, the Contextual Dilation Fusion (CDF) module fuses adjacent multi-layer features and extracts multi-receptive field features, improving adaptability to polyps of different scales and enhancing feature diversity. Second, the Attention-Driven Feature Enhancement (AFE) module suppresses irrelevant background information and strengthens feature representation. Finally, the Dual-path Feature Fusion (DPF) module effectively integrates multi-level features using concatenation and point-wise addition. Extensive experiments on five datasets using four metrics demonstrate the effectiveness and strong generalization ability of the proposed method.