Effective feature learning and fusion of multimodality data using stage‐wise deep neural network for dementia diagnosis

In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects. Multimodality neuroimaging data such as MRI and PET provide valuable...

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Veröffentlicht in:	Human brain mapping Jg. 40; H. 3; S. 1001 - 1016
Hauptverfasser:	Zhou, Tao, Thung, Kim‐Han, Zhu, Xiaofeng, Shen, Dinggang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 15.02.2019
Schlagworte:	Abnormalities Aged Aging Alzheimer's disease Alzheimer's disease (AD) Artificial neural networks Brain Cognitive ability Cognitive Dysfunction - diagnostic imaging Cognitive Dysfunction - genetics Deep Learning Dementia Dementia - diagnostic imaging Dementia - genetics Dementia disorders Diagnosis Diagnostic systems Female Gene polymorphism Heterogeneity Humans Machine learning Magnetic resonance imaging Male Medical imaging Middle Aged mild cognitive impairment (MCI) Multimodal Imaging - methods multimodality data fusion Neurodegenerative diseases Neuroimaging Neuroimaging - methods Neurology Polymorphism Polymorphism, Single Nucleotide Positron emission tomography Representations Risk analysis Risk factors Single-nucleotide polymorphism Training mild cognitive impairment (MCI) deep learning Alzheimer's disease (AD) multimodality data fusion
ISSN:	1065-9471, 1097-0193, 1097-0193
Online-Zugang:	Volltext
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