DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis

doi:10.1049/cit2.12340

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis
	Liu, Shuaiqi 1,2,3; Wang, Siqi 1,2; Sun, Chaolei 1,2; Li, Bing3 ; Wang, Shuihua 4; Li, Fei 1,2
发表期刊	CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
ISSN	2468-6557
	2024-05-03
页码	15
通讯作者	Liu, Shuaiqi(shqliu@hbu.edu.cn) ; Li, Fei(lifei@hbu.edu.cn)
摘要	Diagnosing individuals with autism spectrum disorder (ASD) accurately faces great challenges in clinical practice, primarily due to the data's high heterogeneity and limited sample size. To tackle this issue, the authors constructed a deep graph convolutional network (GCN) based on variable multi-graph and multimodal data (VMM-DGCN) for ASD diagnosis. Firstly, the functional connectivity matrix was constructed to extract primary features. Then, the authors constructed a variable multi-graph construction strategy to capture the multi-scale feature representations of each subject by utilising convolutional filters with varying kernel sizes. Furthermore, the authors brought the non-imaging information into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between subjects. After extracting the deeper features of population graphs using the deep GCN(DeepGCN), the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD patients. The proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I (ABIDE I) dataset, achieving an accuracy of 91.62% and an area under the curve value of 95.74%. These results demonstrated its outstanding performance compared to other ASD diagnostic algorithms.
关键词	machine learning medical image processing medical signal processing
DOI	10.1049/cit2.12340
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province[F2022201055] ; China Postdoctoral[2022M713361] ; Science Foundation Science Research Project of Hebei Province[CXY2024031] ; Natural Science Interdisciplinary Research Program of Hebei University[DXK202102] ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR)[202200007] ; High-Performance Computing Center of Hebei University ; [62172139]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; China Postdoctoral ; Science Foundation Science Research Project of Hebei Province ; Natural Science Interdisciplinary Research Program of Hebei University ; Open Project Program of the National Laboratory of Pattern Recognition (NLPR) ; High-Performance Computing Center of Hebei University
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001217276800001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/58325
专题	多模态人工智能系统全国重点实验室_视频内容安全
通讯作者	Liu, Shuaiqi; Li, Fei
作者单位	1.Hebei Univ, Coll Elect & Informat Engn, Baoding, Peoples R China 2.Machine Vis Technol Innovat Ctr Hebei Prov, Baoding, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing, Peoples R China 4.Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Liu, Shuaiqi,Wang, Siqi,Sun, Chaolei,et al. DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis[J]. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY,2024:15.
APA	Liu, Shuaiqi,Wang, Siqi,Sun, Chaolei,Li, Bing,Wang, Shuihua,&Li, Fei.(2024).DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis.CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY,15.
MLA	Liu, Shuaiqi,et al."DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis".CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2024):15.