文献类型：期刊文献

中文题名：基于双路径交叉融合网络的肺结节CT图像分类方法

英文题名：Pulmonary Nodule Computed Tomography Image Classification Method Based on Dual-Path Cross-Fusion Network

作者：杨萍[1];张鑫[1];温帆[1];田吉[1];何宁[1]

第一作者：杨萍

机构：[1]北京联合大学智慧城市学院,北京100101

第一机构：北京联合大学智慧城市学院

年份：2024

卷号：61

期号：8

起止页码：343-352

中文期刊名：激光与光电子学进展

外文期刊名：Laser & Optoelectronics Progress

收录：CSTPCD;;Scopus;WOS:【ESCI(收录号:WOS:001283351300037)】；北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；

基金：国家自然科学基金(62172045,62272049)。

语种：中文

中文关键词：肺结节良恶性分类;CT图像;局部-全局特征;Transformer;注意力机制

外文关键词：classification of benign and malignant pulmonary nodules;CT image;localglobal feature;Transformer;attention mechanism

摘要：针对肺结节计算机断层(CT)图像具有的细节多样性以及类间相似性的问题,构建了一种集卷积神经网络(Convolutional neural network, CNN)和Transformer优势的双路径交叉融合网络对肺结节进行更精确的分类。首先,以窗口多头自注意力和滑动窗口多头自注意力为基础,构建全局特征块,用于捕获结节的形态特征;以大核注意力为基础构建局部特征块,用于提取结节的纹理、密度等内部特征。其次,设计特征融合块用于融合上一阶段的局部与全局特征,使每一条路径都能获得更综合的判别信息。然后,引入KL(Kullback-leibler)散度来增加不同尺度特征之间的分布差异性,优化网络性能。最后,采用决策层融合的方法获得分类结果。在LIDC-IDRI数据集上进行实验,网络的分类准确率、召回率、精确率、特异性、受试者操作特征(ROC)曲线下的面积(Area under curve, AUC)分别为94.16%、93.93%、93.03%、92.54%、97.02%。实验结果表明,所提方法具有较好的肺结节良恶性分类能力。
Pulmonary nodule computed tomography(CT)images have diverse details and interclass similarity.To address this problem,a dualpath crossfusion network combining the advantages of convolutional neural network(CNN)and Transformer is constructed to classify pulmonary nodules more accurately.First,based on windows multihead selfattention and shifted windows multihead selfattention,a global feature block is constructed to capture the morphological features of nodules;then,a local feature block is constructed based on large kernel attention,which is used to extract internal features such as the texture and density of nodules.A feature fusion block is designed to fuse local and global features of the previous stage so that each path can collect more comprehensive discriminative information.Subsequently,KullbackLeibler(KL)divergence is introduced to increase the distribution difference between features of different scales and optimize network performance.Finally,a decisionlevel fusion method is used to obtain the classification results.Experiments are conducted on the LIDCIDRI dataset,and the network achieves a classification accuracy,recall,precision,specificity,and area under curve(AUC)of 94.16%,93.93%,93.03%,92.54%,and 97.02%,respectively.Experimental results show that this method can classify benign and malignant pulmonary nodules effectively.