Multi-Cluster Self-Paced Diverse Learning and Feature Fusion for Autism Spectrum Disorder Identification With Resting-State fMRI-Reference-Cited by-同舟云学术

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Multi-Cluster Self-Paced Diverse Learning and Feature Fusion for Autism Spectrum Disorder Identification With Resting-State fMRI

Published:2024 Issue: Volume: Page:1-14
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Hao Xiaoke¹^ORCID,Wang Jingyi¹^ORCID,He Zilong¹^ORCID,Guo Yingchun¹^ORCID,Yu Ming¹^ORCID,Liu Feng²^ORCID,Qin Jing³^ORCID,Zhang Daoqiang⁴^ORCID

Affiliation:

1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

2. Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China

3. Centre for Smart Health, School of Nursing, Hong Kong Polytechnic University, Hong Kong, China

4. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province of China

Project of Strategic Importance Scheme of Hong Kong Polytechnic University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/7777658/10483093.pdf?arnumber=10483093

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