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Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play Method

文献类型：期刊论文

作者Fan, Chen-Chen1,2; Yang, Hongjun2; Zhang, Chutian3; Peng, Liang2; Zhou, Xiaohu2; Liu, Shiqi2; Chen, Sheng2,4; Hou, Zeng-Guang2,4,5,6
刊名IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
出版日期2023
卷号31页码:4773-4780
关键词Alzheimer's disease diagnosis graph convolution network plug-and-play structural magnetic resonance imaging
ISSN号1534-4320
DOI10.1109/TNSRE.2023.3337533
通讯作者Yang, Hongjun(hongjun.yang@ia.ac.cn) ; Hou, Zeng-Guang(zengguang.hou@ia.ac.cn)
英文摘要Recent advances in deep learning have led to increased adoption of convolutional neural networks (CNN) for structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) detection. AD results in widespread damage to neurons in different brain regions and destroys their connections. However, current CNN-based methods struggle to relate spatially distant information effectively. To solve this problem, we propose a graph reasoning module (GRM), which can be directly incorporated into CNN-based AD detection models to simulate the underlying relationship between different brain regions and boost AD diagnosis performance. Specifically, in GRM, an adaptive graph Transformer (AGT) block is designed to adaptively construct a graph representation based on the feature map given by CNN, a graph convolutional network (GCN) block is adopted to update the graph representation, and a feature map reconstruction (FMR) block is built to convert the learned graph representation to a feature map. Experimental results demonstrate that the insertion of the GRM in the existing AD classification model can increase its balanced accuracy by more than 4.3%. The GRM-embedded model achieves state-of-the-art performance compared with current deep learning-based AD diagnosis methods, with a balanced accuracy of 86.2%.
WOS关键词ALZHEIMERS-DISEASE ; MRI
资助项目National Natural Science Foundation of China
WOS研究方向Engineering ; Rehabilitation
语种英语
WOS记录号WOS:001122291500004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/57812]  
专题多模态人工智能系统全国重点实验室_医疗机器人
通讯作者Yang, Hongjun; Hou, Zeng-Guang
作者单位1.Chinese Peoples Liberat Army Gen Hosp, Med Big Data Res Ctr, Beijing 100853, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
3.Macau Univ Sci & Technol, Fac Innovat Engn, Dept Engn Sci, Taipa 999078, Macao, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Automation, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
6.Macau Univ Sci & Technol, Macau Inst Syst Engn, CASIA MUST Joint Lab Intelligence Sci & Technol, Taipa 999078, Macao, Peoples R China
推荐引用方式
GB/T 7714		 Fan, Chen-Chen,Yang, Hongjun,Zhang, Chutian,et al. Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play Method[J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,2023,31:4773-4780.
APA Fan, Chen-Chen.,Yang, Hongjun.,Zhang, Chutian.,Peng, Liang.,Zhou, Xiaohu.,...&Hou, Zeng-Guang.(2023).Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play Method.IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING,31,4773-4780.
MLA Fan, Chen-Chen,et al."Graph Reasoning Module for Alzheimer’s Disease Diagnosis: A Plug-and-Play Method".IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 31(2023):4773-4780.

入库方式： OAI收割

来源：自动化研究所

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