Deep Variational Autoencoder for Mapping Functional Brain Networks

In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods to map functional brain networks (FBNs). Recently, it has been proven that deep learning (DL) can be applied on functional magnetic resonance image (fMRI) data with superb representation p...

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Vydané v:	IEEE transactions on cognitive and developmental systems Ročník 13; číslo 4; s. 841 - 852
Hlavní autori:	Qiang, Ning, Dong, Qinglin, Ge, Fangfei, Liang, Hongtao, Ge, Bao, Zhang, Shu, Sun, Yifei, Gao, Jie, Liu, Tianming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.12.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Attention deficit hyperactivity disorder Attention deficit hyperactivity disorder (ADHD) Biological system modeling Brain Brain modeling Classification Data models Datasets Deep learning deep learning (DL) Feature extraction Functional magnetic resonance imaging generative learning Machine learning Magnetic resonance imaging Mapping Medical imaging Representations resting-state functional magnetic resonance image (rfMRI) Unsupervised learning variational autoencoder (VAE)
ISSN:	2379-8920, 2379-8939
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