结合视图感知CNN和Transformer的阿尔茨海默病诊断研究  

Combining view-aware CNN and Transformer for Alzheimer′s disease diagnosis research

作　　者：吴慧东 刘立程[1] 潘丹 Wu Huidong;Liu Licheng;Pan Dan(School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China;School of Electronics and Information,Guangdong Polytechnic Normal University,Guangzhou 510665,China)

机构地区：[1]广东工业大学信息工程学院,广州510006 [2]广东技术师范大学电子与信息学院,广州510665

出　　处：《电子测量技术》2025年第1期145-153,共9页Electronic Measurement Technology

基　　金：国家自然科学基金项目(61976058);广州市科技计划项目(202206010007);广东省科技计划项目(2021B0101220006)。

摘　　要：为解决阿尔茨海默病(AD)患者大脑结构性核磁共振影像(sMRI)病变细微复杂和空间异质性分布引起的病症诊断准确率低的问题,提出了一种结合卷积神经网络(CNN)和Transformer优势的混合架构,用于AD病症诊断。首先,设计了多视图特征编码器,通过构造融合混合注意力机制的视图局部特征提取器分支,从sMRI的冠状面、矢状面和轴向面方向提取潜在互补信息,并通过多视图信息交互学习策略增强病灶区域的语义表征。其次,设计了级联式多尺度融合子网络,逐层融合多尺度特征图以生成更丰富判别信息。最后,利用Transformer编码器建模了全脑sMRI的全局特征表示。在阿尔茨海默病神经影像倡议(ADNI)数据集上的结果显示,本文方法在AD分类和轻度认知障碍(MCI)转化预测任务的准确率分别达到了94.05%和81.59%,优于多种现有方法。To address the low diagnostic accuracy of Alzheimer′s disease(AD)caused by the subtle complexity and spatial heterogeneity of brain lesions in structural magnetic resonance imaging(sMRI)of AD patients,a hybrid architecture that combines the strengths of convolutional neural networks(CNN)and Transformers is proposed for the AD diagnosis.First,a multi-view feature encoder is designed,in which a view local feature extractor with integrated hybrid attention mechanisms is employed to extract complementary information from the coronal,sagittal,and axial views of sMRI.The semantic representation of lesion regions is further enhanced through a multi-view information interaction learning strategy.Second,a cascaded multi-scale fusion subnetwork is designed to progressively fuse multi-scale feature map information,enhancing discriminative ability.Finally,a Transformer encoder is used to model the global feature representation of full-brain sMRI.Results on the Alzheimer′s disease neuroimaging initiative(ADNI)dataset show that the proposed method in this paper achieves classification accuracies of 94.05%for AD and 81.59%for mild cognitive impairment(MCI)conversion prediction,outperforming several existing methods.

关 键 词：阿尔兹海默病 结构性核磁共振成像 混合架构 多视图信息 多尺度特征 

分 类 号：TN911.73[电子电信—通信与信息系统]