A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes

Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Re...

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Vydané v:	Frontiers in neuroscience Ročník 12; s. 491
Hlavní autori:	Li, Hailong, Parikh, Nehal A., He, Lili
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 24.07.2018 Frontiers Media S.A
Predmet:	Accuracy Algorithms Alzheimer's disease Artificial intelligence Attention deficit hyperactivity disorder Autism Brain mapping Classification Consortia Deep learning functional connectomes Functional magnetic resonance imaging Knowledge Learning algorithms Machine learning Medical imaging Neural networks Neuroimaging Neurological diseases Neuroscience Neurosciences NMR Nuclear magnetic resonance resting-state functional MRI Schizophrenia stacked sparse autoencoder Studies Transfer learning United States > US deep learning transfer learning stacked sparse autoencoder resting-state functional MRI functional connectomes neural networks autism spectrum disorder
ISSN:	1662-453X, 1662-4548, 1662-453X
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