基于注意力机制和双分支网络的胸部疾病分类  

Classification of Thoracic Diseases Based on Attention Mechanisms and Two-branch Networks

作　　者：宋子岩 罗川 李天瑞[2] 陈红梅[2] SONG Ziyan;LUO Chuan;LI Tianrui;CHEN Hongmei(College of Computer Science,Sichuan University,Chengdu 610065,China;School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]四川大学计算机学院,成都610065 [2]西南交通大学计算机与人工智能学院,成都611756

出　　处：《计算机科学》2024年第S02期209-214,共6页Computer Science

基　　金：国家自然科学基金(62076171,62376230);四川省自然科学基金(2022NSFSC0898)。

摘　　要：基于胸部X光片的胸部疾病分类对于提高诊断准确率、减轻医疗卫生系统压力具有重要意义。不同胸部疾病的发病区域尺寸存在巨大差异,是基于胸部X光片的胸部疾病分类任务面临的主要挑战。在对发病区域较小的疾病进行分类时,图像中大部分区域均为噪声区域,传统方法难以有效应对疾病间巨大的尺寸差异。针对这一问题,提出了一种结合多尺度特征的掩码构造方法,以DenseNet-121为特征提取器,构建了一个双分支网络,使用全局网络进行总体分类,并将微小病变区域送入局部分支以减轻噪声区域的干扰,最终利用基于注意力机制分支特征融合模块自适应地融合两个分支的分类特征信息。在ChestX-ray14数据集上进行了对比实验、消融实验和参数敏感性分析,结果表明,所提方法对14种胸部疾病分类的平均AUC高于现有方法,具有有效性且对参数不敏感。Thoracic disease classification based on chest radiographs is important to improve diagnostic accuracy and reduce the pressure on the healthcare system.The huge variation in the size of the regions of different thoracic diseases is the main challenge in the chest radiograph-based classification of thoracic diseases.When classifying diseases with small onset regions,most of the regions in the image are noisy regions,and it is difficult for traditional methods to cope with the huge size differences among di-seases effectively.To address this problem,a mask construction method combining multi-scale features is proposed,using DenseNet-121 as the feature extractor,a two-branch network is constructed,in which the global network is used for the overall classification,and tiny lesion regions are fed into the local branches to mitigate the interference of noisy regions.The branch feature fusion module based on the attentional mechanism is used to fuse the classification features from the two branches’information adaptively.Comparison experiments,ablation experiments,and parameter sensitivity analyses are performed on the ChestX-ray14 dataset.The experimental results show that the average AUC of the proposed method for classifying 14 thoracic diseases is higher than that of the existing methods,which is effective and parameter insensitive.

关 键 词：胸部疾病分类 深度学习 胸部X光片 注意力机制 特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]