基于三维多视角挤压激励卷积神经网络的肺结节良恶性分类研究  

Research on classification of benign and malignant lung nodules based on threedimensional multi-view squeeze-and-excitation convolutional neural network

作　　者：杨杨[1] 李晓琴[1] 韩振波 付继鹏 高斌[1] YANG Yang;LI Xiaoqin;HAN Zhenbo;FU Jipeng;GAO Bin(Faculty of Environment and Life,Beijing University of Technology,Beijing 100124,P.R.China)

机构地区：[1]北京工业大学环境与生命学部,北京100124

出　　处：《生物医学工程学杂志》2022年第3期452-461,共10页Journal of Biomedical Engineering

基　　金：国家重点研发计划资助项目(2017YFC0111104);国家自然科学基金资助项目(61931013)。

摘　　要：肺癌是对人类健康威胁最大的肿瘤疾病,早期发现对于患者的生存和康复至关重要。现有方法采用二维多视角框架学习肺结节特征并简单集成多个视角特征实现肺结节良恶性分类。然而,这些方法存在不能有效捕捉空间特性和忽略了多个视角的差异性问题。因此,本文提出三维(3D)多视角卷积神经网络(MVCNN)框架,为进一步解决多视角模型中各视角的差异性问题,在特征融合阶段引入挤压激励(SE)模块,构建了3D多视角挤压激励卷积神经网络(MVSECNN)模型。最后,采用统计学方法对模型预测与医生注释结果进行分析。在独立测试集中,模型的分类准确率和灵敏度分别为96.04%和98.59%,均高于目前已有方法;模型预测与病理诊断的一致性分数为0.948,显著高于医生注释结果与病理诊断的一致性。本文所提方法可以有效地学习结节空间异质性和解决多视角差异性问题,同时实现了肺结节良恶性分类,对于辅助医生进行临床诊断具有重要意义。Lung cancer is the most threatening tumor disease to human health. Early detection is crucial to improve the survival rate and recovery rate of lung cancer patients. Existing methods use the two-dimensional multi-view framework to learn lung nodules features and simply integrate multi-view features to achieve the classification of benign and malignant lung nodules. However, these methods suffer from the problems of not capturing the spatial features effectively and ignoring the variability of multi-views. Therefore, this paper proposes a three-dimensional(3D) multi-view convolutional neural network(MVCNN) framework. To further solve the problem of different views in the multi-view model, a 3D multi-view squeeze-and-excitation convolution neural network(MVSECNN) model is constructed by introducing the squeeze-and-excitation(SE) module in the feature fusion stage. Finally, statistical methods are used to analyze model predictions and doctor annotations. In the independent test set, the classification accuracy and sensitivity of the model were 96.04% and 98.59% respectively, which were higher than other state-of-the-art methods. The consistency score between the predictions of the model and the pathological diagnosis results was 0.948, which is significantly higher than that between the doctor annotations and the pathological diagnosis results. The methods presented in this paper can effectively learn the spatial heterogeneity of lung nodules and solve the problem of multi-view differences. At the same time, the classification of benign and malignant lung nodules can be achieved, which is of great significance for assisting doctors in clinical diagnosis.

关 键 词：图像处理 深度学习 注意力机制 肺结节 一致性分析 

分 类 号：R734.2[医药卫生—肿瘤] TP183[医药卫生—临床医学] TP391.41[自动化与计算机技术—控制理论与控制工程]