Boundary Refinement Network for Polyp Segmentation With Deformable Attention

Early and accurate polyp segmentation is crucial for the diagnosis and treatment of colorectal cancer. However, polyp segmentation faces many challenges: different polyp sizes, complex shapes, and ambiguous intestinal wall boundaries. To solve these problems, we propose a novel polyp segmentation network named DeformSegNet. Specifically, we first introduce a polyp perception module (PPM), which combines the dynamic multi-kernel spatial selection network (DMS-Net) and a transformer encoder to effectively locate polyps of different sizes. Next, we design a deformation-aware separable module (DSM), which consists of deformable attention that adaptively adjusts the sampling position, enabling the network to adapt to complex and diverse polyp boundaries. Finally, a cross-attention aggregation module (CAAM) effectively retains low-level features, further enhancing the boundary features and suppressing false positives. DeformSegNet achieves competitive segmentation accuracy on five polyp datasets, demonstrating excellent learning and generalization capabilities.