基于多任务联合注意力的结肠息肉分割网络OA北大核心CSTPCD

Colon polyps have the characteristics of unclear boundaries and varying sizes,colors,and shapes,making it difficult to improve their segmentation performance using deep learning methods.A colon polyp segmentation network CPMJA-Net based on multi-task joint attention is proposed to improve the accuracy of polyp segmentation.To address the problem of Transformers lacking mechanisms to improve local information exchange,a cascaded fusion module is designed to enhance the local feature representation of the network,which aids in the recognition and restoration of polyp edges.Inspired by the multi-head Self-Attention mechanism,a Multi-task Attention Module(MAM)is constructed,and the feature maps obtained from different modules are gradually fused using a progressive fusion method to highlight key information and suppress interference information.A Joint Attention Module(JAM)is designed to use the contour information of advanced features to filter out detailed features that are conducive to edge segmentation from low-level features and aggregate them with the contour of polyps to obtain more accurate edge segmentation results to better aggregate the advanced and low-level features of images.The experimental results show that CPMJA-Net has the best performance of all four public datasets.Compared with the suboptimal algorithm,the mDice coefficient of CPMJA-Net has improved by 0.7,0.8,0.4,and 0.4 percentage points on the Kvasir,CVC-CilinicDB,CVC-ColonDB,and ETIS datasets,respectively.In addition,the mean Intersection over Union(mIoU)increased by 1.6,1.2,0.6,and 0.5 percentage points,respectively.Experiments have shown that CPMJA-Net improves over-segmentation,compensates for attention mechanism shortcomings,and improves the decoder's ability to recover details.