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

文献类型：期刊论文

作者Liu, Shuaiqi1,2,3; Wang, Siqi1,2; Sun, Chaolei1,2; Li, Bing3; Wang, Shuihua4; Li, Fei1,2
刊名CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
出版日期2024-05-03
页码15
关键词machine learning medical image processing medical signal processing
ISSN号2468-6557
DOI10.1049/cit2.12340
通讯作者Liu, Shuaiqi(shqliu@hbu.edu.cn) ; Li, Fei(lifei@hbu.edu.cn)
英文摘要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.
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]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001217276800001
出版者WILEY
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/58324]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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