AUTHOR=Zhu Peng-Cheng , Wan Jing-Jing , Shao Wei , Meng Xian-Chun , Chen Bo-Lun TITLE=Colorectal image analysis for polyp diagnosis JOURNAL=Frontiers in Computational Neuroscience VOLUME=18 YEAR=2024 URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1356447 DOI=10.3389/fncom.2024.1356447 ISSN=1662-5188 ABSTRACT=

Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems, in this paper, we propose a novel colorectal image analysis method for polyp diagnosis via PAM-Net. Specifically, a parallel attention module is designed to enhance the analysis of colorectal polyp images for improving the certainties of polyps. In addition, our method introduces the GWD loss to enhance the accuracy of polyp diagnosis from the perspective of polyp location. Extensive experimental results demonstrate the effectiveness of the proposed method compared with the SOTA baselines. This study enhances the performance of polyp detection accuracy and contributes to polyp detection in clinical medicine.