Multi-scale Information Sharing and Selection Network with Boundary Attention for Polyp Segmentation
This addresses polyp segmentation for colorectal cancer diagnosis, with incremental improvements over existing methods.
The paper tackled polyp segmentation in colonoscopy images by proposing MISNet, which improved accuracy and clarity, outperforming state-of-the-art methods on five datasets.
Polyp segmentation for colonoscopy images is of vital importance in clinical practice. It can provide valuable information for colorectal cancer diagnosis and surgery. While existing methods have achieved relatively good performance, polyp segmentation still faces the following challenges: (1) Varying lighting conditions in colonoscopy and differences in polyp locations, sizes, and morphologies. (2) The indistinct boundary between polyps and surrounding tissue. To address these challenges, we propose a Multi-scale information sharing and selection network (MISNet) for polyp segmentation task. We design a Selectively Shared Fusion Module (SSFM) to enforce information sharing and active selection between low-level and high-level features, thereby enhancing model's ability to capture comprehensive information. We then design a Parallel Attention Module (PAM) to enhance model's attention to boundaries, and a Balancing Weight Module (BWM) to facilitate the continuous refinement of boundary segmentation in the bottom-up process. Experiments on five polyp segmentation datasets demonstrate that MISNet successfully improved the accuracy and clarity of segmentation result, outperforming state-of-the-art methods.