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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Vydáno v:	Pattern recognition Ročník 134; s. 109106
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.02.2023
Témata:	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
On-line přístup:	Získat plný text
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