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DeepGCN based on variable multi-graph and multimodal data for ASD diagnosis | |
Liu, Shuaiqi1,2,3; Wang, Siqi1,2; Sun, Chaolei1,2; Li, Bing3![]() | |
发表期刊 | CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
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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. |
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