融合改进变分自编码器与影像组学的X光片肺部疾病筛查算法  

X-rays lung disease screening algorithm based on the fusion ofimproved variational autoencoder and radiomics

作　　者：冯筠[1] 牛怡 杨晨希 沈聪 郭佑民[2] FENG Jun;NIU Yi;YANG Chenxi;SHEN Cong;GUO Youmin(School of Information Science and Technology,Northwest University,Xi’an 710127,China;Department of Medical Imaging,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,China)

机构地区：[1]西北大学信息科学与技术学院,陕西西安710127 [2]西安交通大学第一附属医院医学影像科,陕西西安710061

出　　处：《西北大学学报（自然科学版）》2023年第3期313-324,共12页Journal of Northwest University（Natural Science Edition）

基　　金：国家自然科学基金(62073260);陕西省教育厅科研计划项目(21JK0927)。

摘　　要：计算机辅助技术在肺部疾病筛查方面已经取得显著成效,然而现有研究大多面向已知类型的疾病进行建模,对未知类型疾病极易带来误诊及漏诊风险,且主要以追求高准确率为目标,对误诊及漏诊未加以约束,导致其难以应用于实际临床场景。针对以上问题,该文提出更适用于临床的计算机辅助肺部疾病筛查目标,即保证零漏诊率的同时降低误诊率。为完成上述肺部疾病筛查目标,该文基于单类别分类思想提出改进变分自编码网络对肺部疾病初筛,并提取X光片图像的深度编码特征,接着,融合基于医生经验的影像组学特征以及深度学习特征之间的互补优势,构建一个集成学习模型,最终完成肺部疾病的筛查。在仅有正常X光片图像参与训练的情况下,提升了所构建模型的分类效果,降低了模型的漏诊率。实验结果AUC值为0.9848±0.0023,漏诊率为0时,误诊率降低至0.1498±0.0057,证明该方法可以有效达到该文的肺部疾病筛查目标。与此同时,对比了所构建的集成模型以及单独的深度学习模型的筛查效果,发现集成模型明显优于深度学习模型,进一步凸显了融合医生经验的有效性。Computer-aided technologies have achieved excellent results in lung diseases screening.However,most of the existing studies models the known type of disease,which can lead to the risk of misdiagnosis and missed diagnosis to the unknown type of disease.In addition,current research primarily focuses on achieving high accuracy without solving the problems of misdiagnosis and missed diagnosis,making it difficult to apply in clinical practice.This paper proposes a more clinically applicable goal for computer-aided pulmonary disease screening by combing the doctor’s diagnosis process:to ensure a zero missed diagnosis rate while reducing the misdiagnosis rate.To achieve the screening goal mentioned above,this paper proposes an improved variational autoencoder network based on one-class classification for preliminary screening of lung diseases and extraction of deep encoding features from X-ray images.Then,by combining the complementary advantages between radiomics features based on doctors’experience and deep learning features,an ensemble learning model is constructed to complete the final lung disease screening.The method in this study improved the classification performance and reduced the missed diagnosis rate with only normal X-ray images involved in training.Experimental results show that the proposed method achieves an AUC value of 0.9848±0.0023 and reduces the misdiagnosis rate to 0.1498±0.0057 when the missed diagnosis rate is zero,further validating that the method can effectively achieve the pulmonary diseases screening goal of this paper.At the same time,we compared the screening results of the ensemble model and individual deep learning model.We found that the results of the ensemble model was significantly better than the deep learning model,which further highlights the effectiveness of integrating doctors’experience.

关 键 词：肺部疾病筛查 胸部X光片 影像组学 单类别分类 集成学习 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术]