计算机工程2024，Vol.50Issue(10)：35-50,16.DOI:10.19678/j.issn.1000-3428.0068719
阿尔茨海默病的图神经网络分类方法研究进展
Research Progress on Graph Neural Network Classification Methods for Alzheimer s Disease
摘要
Abstract
Alzheimer's Disease(AD)is an irreversible neurodegenerative disorder that leads to gradual cognitive decline.The evolution of AD symptoms can be long,with subtle changes in biomarkers in brain regions that are detectable by different neuroimaging modalities;however,early detection is challenging.Given the high complexity of neuroimaging data and the irregularity of brain networks,traditional machine learning,and deep neural network models exhibit many shortcomings,and the development of Computer-Aided Diagnostic(CAD)models based on Graph Neural Network(GNN)can be beneficial for probing biomarkers and analyzing neuroimaging patterns in non-Euclidean space.First,a detailed investigation and overview of AD prediction based on GNN classification methods is carried out.Subsequently,an analysis is conducted from the two perspectives of single-and multi-modal data,with a focus on discussing and analyzing the processes of data extraction,brain network modeling,feature learning,and information fusion within the context of single-and multi-modal data applications.A performance evaluation is provided for certain methods.Finally,the primary challenges and future research directions for the application of GNNs in AD diagnosis are outlined to provide beneficial suggestions for further research on AD-assisted diagnosis.关键词
图神经网络/阿尔茨海默病/辅助诊断/神经成像/多模态数据Key words
Graph Neural Network(GNN)/Alzheimer's Disease(AD)/assisted diagnosis/neuroimaging/multi-modal data分类
信息技术与安全科学引用本文复制引用
顾宇衡,潘嘉诚,钱江波,董一鸿..阿尔茨海默病的图神经网络分类方法研究进展[J].计算机工程,2024,50(10):35-50,16.基金项目
国家自然科学基金(62271274) （62271274）
宁波市公益类科技计划项目(2023S023) （2023S023）
宁波市自然科学基金(2023J114). （2023J114）
