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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Vydáno v:	Human brain mapping Ročník 40; číslo 3; s. 1001 - 1016
Hlavní autoři:	Zhou, Tao, Thung, Kim‐Han, Zhu, Xiaofeng, Shen, Dinggang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken, USA John Wiley & Sons, Inc 15.02.2019
Témata:	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
On-line přístup:	Získat plný text
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