融合Transformer自适应特征选择的结直肠息肉分割  

Colorectal polyp segmentation via Transformerbased adaptive feature selection

作　　者：梁礼明 康婷 王成斌 陈康泉 李俞霖 Liang Liming;Kang Ting;Wang Chengbin;Chen Kangquan;Li Yulin(College of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China)

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《光电工程》2025年第3期70-82,共13页Opto-Electronic Engineering

基　　金：国家自然科学基金资助项目(51365017,61463018);江西省自然科学基金资助项目(20192BAB205084);江西省教育厅科学技术研究青年项目(GJJ2200848)。

摘　　要：针对结直肠息肉分割中区域误分割和目标定位精度不足等挑战,本文提出一种融合Transformer自适应特征选择的结直肠息肉分割算法。首先通过Transformer编码器提取多层次特征表示,涵盖从细粒度到高层语义的多尺度信息;其次设计双重聚焦注意力模块,通过融合多尺度信息、空间注意力和局部细节特征,增强特征表达与辨识能力,显著提升病灶区域定位精度;再次引入分层特征融合模块,采用层次化聚合策略,加强局部与全局特征的融合,强化对复杂区域特征的捕捉,有效减少误分割现象;最后结合动态特征选择模块的自适应筛选与加权机制,优化多分辨率特征表达,去除冗余信息,聚焦关键区域。在Kvasir、CVC-ClinicDB、CVC-ColonDB和ETIS数据集上进行实验验证,其Dice系数分别达到0.926、0.941、0.814和0.797。实验结果表明,本文算法在结直肠息肉分割任务中具有优越性能和应用价值。To address challenges such as regional mis-segmentation and insufficient target localization accuracy in colorectal polyp segmentation,this paper proposes a colorectal polyp segmentation algorithm that integrates adaptive feature selection based on a Transformer.Firstly,the Transformer encoder is employed to extract multi-level feature representations,capturing multi-scale information from fine-grained to high-level semantics.Secondly,a dual-focus attention module is designed to enhance feature representation and recognition capabilities by integrating multi-scale information,spatial attention,and local detail features,significantly improving the localization accuracy of lesion areas.Thirdly,a hierarchical feature fusion module is introduced,which adopts a hierarchical aggregation strategy to strengthen the fusion of local and global features,enhancing the capture of complex regional features and effectively reducing mis-segmentation.Finally,a dynamic feature selection module is incorporated with adaptive selection and weighting mechanisms to optimize multi-resolution feature representation,eliminate redundant information,and focus on key areas.Experiments conducted on the Kvasir,CVC-ClinicDB,CVC-ColonDB,and ETIS datasets achieved Dice coefficients of 0.926,0.941,0.814,and 0.797,respectively.The experimental results demonstrate that the proposed algorithm exhibits superior performance and application value in the task of colorectal polyp segmentation.

关 键 词：结直肠息肉 TRANSFORMER 双重聚焦注意力模块 动态特征选择模块 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]