光学精密工程2024，Vol.32Issue(16)：2523-2536,14.DOI:10.37188/OPE.20243216.2523
渐进式CNN-Transformer语义补偿息肉分割网络
Progressive CNN-transformer semantic compensation network for polyp segmentation
摘要
Abstract
In response to the problem of low segmentation accuracy in colon polyp images due to varying sizes,complex shapes,and unclear boundaries between polyps and mucosa,a progressive CNN-Trans-former semantic compensation polyp segmentation network was proposed to improve the segmentation ac-curacy of colon polyps.In order to better utilize the local features from the CNN encoder and the global features from the Transformer encoder,a same-layer feature interaction coupling module was designed to adaptively fuse features from the CNN and Transformer encoders in both spatial and channel dimensions through grouped interaction coupling.Then,to address the issue of semantic loss caused by upsampling during the decoding process,a Query-based semantic compensation module was designed.This module gradually integrates and distributes image semantics through a set of learnable descriptors,effectively en-hancing the network's feature discrimination capability.The experimental results show that the proposed network achieved mDice scores of 94.23%,90.36%,92.93%,and 80.26%on the CVC-ClinicDB,CVC-300,Kvasir,and CVC-ColonDB public datasets,respectively.The mIoU scores are 89.87%,83.75%,88.21%,and 72.09%,respectively.Compared to existing segmentation networks,the pro-posed network can effectively improve the effectiveness of polyp segmentation while ensuring its general-ization.关键词
息肉分割/卷积神经网络/Transformer/特征交互/语义补偿Key words
polyp segmentation/convolutional neural network/transformer/feature interaction/seman-tic compensation分类
计算机与自动化引用本文复制引用
李大湘,李登辉,刘颖,唐垚..渐进式CNN-Transformer语义补偿息肉分割网络[J].光学精密工程,2024,32(16):2523-2536,14.基金项目
国家自然科学基金(No.62071379) （No.62071379）
陕西省自然科学基金(No.2019JM-604) （No.2019JM-604）
