BGL-Net: A brain-inspired global-local information fusion network for Alzheimer’s disease based on sMRI | |
Fan, Chen-Chen3,4; Yang, Hongjun4; Peng, Liang4; Zhou, Xiao-Hu4; Ni, Zhen-Liang3,4; Zhou, Yan-Jie3,4; Chen, Sheng3,4; Hou, Zeng-Guang1,2,3,4 | |
刊名 | IEEE Transactions on Cognitive and Developmental Systems |
2022 | |
卷号 | doi: 10.1109/TCDS.2022.3204782页码:1-9 |
DOI | 10.1109/TCDS.2022.3204782 |
英文摘要 | Alzheimer’s Disease (AD) is an irreversible neurodegenerative disease, the most common form of dementia, affecting millions worldwide. Neuroimaging-based early AD diagnosis has become an effective approach, especially by using structural Magnetic Resonance Imaging (sMRI). The convolutional neural network (CNN) based method is challenging to learn dependencies between spatially distant positions in the various brain regions due to its local convolution operation. In contrast, the graph convolutional network (GCN) based work can connect the brain regions to capture global information but is not sensitive to the local information in a single brain region. Unlike a separate CNN or GCN-based method, we proposed a brain-inspired global-local information fusion network (BGL-Net) to diagnose AD. It essentially inherits the advantages of both CNN and GCN. The experiments on three public datasets demonstrate the effectiveness and robustness of our BGL-Net. Our method achieved the best performance on three popular public datasets compared with the existing CNN and GCN-based methods. In addition, our visualization results of the learned brain connection on AD and normal people agree with many current AD clinical research. |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/51863] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
通讯作者 | Hou, Zeng-Guang |
作者单位 | 1.CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Institute of Sys tems Engineering, Macau University of Science and Technology, China. 2.CAS Center for Excellence in Brain Science and Intelli gence Technology, Beijing 100190, China. 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. 4.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. |
推荐引用方式 GB/T 7714 | Fan, Chen-Chen,Yang, Hongjun,Peng, Liang,et al. BGL-Net: A brain-inspired global-local information fusion network for Alzheimer’s disease based on sMRI[J]. IEEE Transactions on Cognitive and Developmental Systems,2022,doi: 10.1109/TCDS.2022.3204782:1-9. |
APA | Fan, Chen-Chen.,Yang, Hongjun.,Peng, Liang.,Zhou, Xiao-Hu.,Ni, Zhen-Liang.,...&Hou, Zeng-Guang.(2022).BGL-Net: A brain-inspired global-local information fusion network for Alzheimer’s disease based on sMRI.IEEE Transactions on Cognitive and Developmental Systems,doi: 10.1109/TCDS.2022.3204782,1-9. |
MLA | Fan, Chen-Chen,et al."BGL-Net: A brain-inspired global-local information fusion network for Alzheimer’s disease based on sMRI".IEEE Transactions on Cognitive and Developmental Systems doi: 10.1109/TCDS.2022.3204782(2022):1-9. |
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