PDCA-Net: Parallel dual-channel attention network for polyp segmentation

Abstract

Accurate segmentation of polyps in colonoscopy images is crucial for the diagnosis and cure of colorectal cancer. Although various deep learning methods have been proposed and have shown promising performance, accurately distinguishing between polyp and mucosal boundaries remains a challenge. In this work, we propose a Parallel Dual-Channel Attention Network (PDCA-Net) for polyp segmentation. This method utilizes the mapping transformations to adaptively encapsulate the global dependency from superpixel into pixels, enhancing the model’s ability to localize foreground and background regions. Specifically, we first design a parallel spatial and channel attention fusion module to capture the global dependencies at the superpixel level from the spatial and channel dimensions. Furthermore, an adaptive associative mapping module is proposed to encapsulate the global dependencies of superpixels into each pixel through a coarse-to-fine learning strategy. Extensive experiments demonstrate that the proposed PDCA-Net effectively improves the segmentation performance and achieves new state-of-the-art results (i.e., 0.815, 0.936, 0.945, and 0.838 mDice, 0.744, 0.891, 0.900, and 0.765 mIoU on the ETIS, Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB). Our code is available at https://github.com/lzucg/PDCA-Net.