Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis-Reference-Cited by-同舟云学术

Exploring interpretable graph convolutional networks for autism spectrum disorder diagnosis

Published:2022-11-04 Issue:4 Volume:18 Page:663-673
ISSN:1861-6429
Container-title:International Journal of Computer Assisted Radiology and Surgery
language:en
Short-container-title:Int J CARS

Author:

Li Lanting,Wen Guangqi,Cao Peng,Liu Xiaoli,R. Zaiane Osmar,Yang Jinzhu

Funder

National Natural Science Foundation of China

Science Project of Liaoning province

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11548-022-02780-3.pdf

Reference30 articles.

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3. Kazeminejad A, Sotero RC (2019) Topological properties of resting-state fmri functional networks improve machine learning-based autism classification. Front Neurosci 12:1018

4. Van Den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on resting-state fmri functional connectivity. Eur Neuropsychopharmacol 20(8):519–534

5. Welling M, Kipf TN (2017) Semi-supervised classification with graph convolutional networks. In: J. international conference on learning representations: ICLR 2017

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