AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning

•Multi-atlas functional connectivity is calculated as the original feature representation.•Multi-atlas deep feature representation is extracted by a deep learning method.•An ensemble learning strategy is proposed to perform the final ASD identification task.•Our proposed method achieves classificati...

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Veröffentlicht in:	Journal of neuroscience methods Jg. 343; S. 108840
Hauptverfasser:	Wang, Yufei, Wang, Jianxin, Wu, Fang-Xiang, Hayrat, Rahmatjan, Liu, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier B.V 01.09.2020
Schlagworte:	Autism spectrum disorder identification Ensemble learning Functional connectivity Functional magnetic resonance imaging Stacked denoising autoencoder Functional connectivity Functional magnetic resonance imaging Ensemble learning Stacked denoising autoencoder Autism spectrum disorder identification
ISSN:	0165-0270, 1872-678X, 1872-678X
Online-Zugang:	Volltext
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