MGF‐net: Multi‐channel group fusion enhancing boundary attention for polyp segmentation.

Background: Colonic polyps are the most prevalent neoplastic lesions detected during colorectal cancer screening, and timely detection and excision of these precursor lesions is crucial for preventing multiple malignancies and reducing mortality rates. Purpose: The pressing need for intelligent polyp detection has led to the development of a high‐precision intelligent polyp segmentation network designed to improve polyp screening rates during colonoscopies. Methods: In this study, we employed ResNet50 as the backbone network and embedded a multi‐channel grouping fusion encoding module in the third to fifth stages to extract high‐level semantic features of polyps. Receptive field modules were utilized to capture multi‐scale features, and grouping fusion modules were employed to capture salient features in different group channels, guiding the decoder to generate an initial global mapping with improved accuracy. To refine the segmentation of the initial global mapping, we introduced an enhanced boundary weight attention module that adaptively thresholds the initial global mapping using learnable parameters. A self‐attention mechanism was then utilized to calculate the long‐distance dependency relationship of the polyp boundary area, resulting in an output feature map with enhanced boundaries that effectively refines the boundary of the target area. Results: We carried out contrast experiments of MGF‐Net with mainstream polyp segmentation networks on five public datasets of ColonDB, CVC‐ColonDB, CVC‐612, Kvasir, and ETIS. The results demonstrate that the segmentation accuracy of MGF‐Net is significantly improved on the datasets. Furthermore, a hypothesis test was conducted to assess the statistical significance of the computed results. Conclusions: Our proposed MGF‐Net outperforms existing mainstream baseline networks and presents a promising solution to the pressing need for intelligent polyp detection. The proposed model is available at https://github.com/xiefanghhh/MGF‐NET. [ABSTRACT FROM AUTHOR]