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Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network
Chen, Lin1; Qiao, Hezhe1,2; Zhu, Fan1
2022-04-26
摘要Numerous artificial intelligence (AI) based approaches have been proposed for automatic Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging (sMRI). Previous studies extract features from the whole brain or individual slices separately, ignoring the properties of multi-view slices and feature complementarity. For this reason, we present a novel AD diagnosis model based on the multiview-slice attention and 3D convolution neural network (3D-CNN). Specifically, we begin by extracting the local slice-level characteristic in various dimensions using multiple sub-networks. Then we proposed a slice-level attention mechanism to emphasize specific 2D-slices to exclude the redundancy features. After that, a 3D-CNN was employed to capture the global subject-level structural changes. Finally, all these 2D and 3D features were fused to obtain more discriminative representations. We conduct the experiments on 1,451 subjects from ADNI-1 and ADNI-2 datasets. Experimental results showed the superiority of our model over the state-of-the-art approaches regarding dementia classification. Specifically, our model achieves accuracy values of 91.1 and 80.1% on ADNI-1 for AD diagnosis and mild cognitive impairment (MCI) convention prediction, respectively.
关键词Alzheimer's disease (AD) disease prognosis multi-view-slice attention 3D convolution neural network brain sMRI image
DOI10.3389/fnagi.2022.871706
发表期刊FRONTIERS IN AGING NEUROSCIENCE
ISSN1663-4365
卷号14页码:13
通讯作者Chen, Lin(chenlin@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000795098300001
语种英语