基于注意力与标签相关性的胸部X光片疾病分类  

Chest X-ray Disease Classification Based on Attentional and Label Correlation

作　　者：王烨楠 程远志 史操 许灿辉 WANG Ye-Nan;CHENG Yuan-Zhi;SHI Cao;XU Can-Hui(College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao 266061,China)

机构地区：[1]青岛科技大学信息科学技术学院,青岛266061

出　　处：《计算机系统应用》2023年第4期104-111,共8页Computer Systems & Applications

基　　金：国家自然科学基金(61806107,61702135)。

摘　　要：针对传统的胸部辅助诊断系统在胸部X光片疾病分类方面图像特征提取效果差、平均准确率低等问题,提出了一个注意力机制和标签相关性结合的多层次分类网络.网络的训练分为两个阶段,在阶段1为了提高网络特征提取能力,引入注意力机制并构建一个双分支特征提取网络,实现综合特征的提取,在阶段2考虑到多标签分类中标签之间相关性等问题,利用图卷积神经网络对标签相关关系进行建模,并与阶段1的特征提取结果进行结合,以实现对胸部X光片疾病的多标签分类任务.实验结果表明,本方法在ChestX-ray14数据集上各类疾病的加权平均AUC达到0.827,有助于辅助医生进行胸部疾病的诊断,有一定的临床应用价值.Traditional chest-aided diagnosis systems have poor image feature extraction effects and low average accuracy in disease classification based on chest X-ray images.In view of these problems,a multi-level classification network that combines an attention mechanism and label correlation is proposed.The training of the network is divided into two stages.In stage one,in order to improve the feature extraction capability of the network,an attention mechanism is introduced,and a two-branch feature extraction network is constructed to realize the extraction of comprehensive features.In stage two,according to the correlation between labels and other issues in multi-label classification,a graph convolutional neural network is used to model the label correlation,which is then combined with the feature extraction results obtained in stage one,so as to achieve the multi-label classification task of diseases based on chest X-ray images.The experimental results show that the weighted average AUC of diseases by the proposed method on the ChestX-ray14 dataset reaches 0.827.Therefore,the method can assist doctors in diagnosing chest diseases and has certain clinical application value.

关 键 词：胸部疾病 多标签分类 标签相关性 深度学习 

分 类 号：R816.4[医药卫生—放射医学] O434.19[医药卫生—临床医学] TP391.41[机械工程—光学工程]