Dual-View Connectivity Analysis via Dynamic Graph Transformer Network for ASD Diagnosis-Reference-Cited by-同舟云学术

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Dual-View Connectivity Analysis via Dynamic Graph Transformer Network for ASD Diagnosis

Published:2024-05-27 Issue: Volume:29 Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Guan Zihao¹,Yu Jiaming¹,Shi Zhenshan²,Liu Xiumei³,Yang Changcai¹,Chen Riqing¹,Wei Lifang¹

Affiliation:

1. Fujian Agriculture and Forestry University,College of Computer and Information Science,China

2. the First Affiliated Hospital of Fujian Medical University,Department of Radiology,China

3. Fujian Children’s Hospital,Developmental and Behavior Pediatrics Department,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635496.pdf?arnumber=10635496

Reference18 articles.

1. Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer’s disease

2. Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification

3. Diagnosis: Redefining autism

4. Using DeepGCN to identify the autism spectrum disorder from multi-site resting-state data

5. Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification

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