Multi-scale enhanced graph convolutional network for mild cognitive impairment detection

•We design a MCI-graph framework which integrates both non-image information and image information. We use LWCC to extract the feature to avoid the high dimensional features.•We devise various parallel GCN layers using multiple inputs from the random walk embedding theory, which is able to identify...

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Bibliographic Details

Published in:	Pattern recognition Vol. 134; p. 109106
Main Authors:	Lei, Baiying, Zhu, Yun, Yu, Shuangzhi, Hu, Huoyou, Xu, Yanwu, Yue, Guanghui, Wang, Tianfu, Zhao, Cheng, Chen, Shaobin, Yang, Peng, Song, Xuegang, Xiao, Xiaohua, Wang, Shuqiang
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.02.2023
Subjects:	Mild cognitive impairment detection Multi-scale enhanced graph convolutional network Multimodal brain connectivity networks Mild cognitive impairment detection Multimodal brain connectivity networks Multi-scale enhanced graph convolutional network
ISSN:	0031-3203, 1873-5142
Online Access:	Get full text
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