Transfer learning‐trained convolutional neural networks identify novel MRI biomarkers of Alzheimer's disease progression

Transfer learning‐trained convolutional neural networks identify novel MRI biomarkers of Alzheimer's disease progression

Abstract Introduction Genome‐wide association studies (GWAS) for late onset Alzheimer's disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity. Methods We use a transf...

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Bibliographic Details
Main Authors: Yi Li, Annat Haber, Christoph Preuss, Cai John, Asli Uyar, Hongtian Stanley Yang, Benjamin A. Logsdon, Vivek Philip, R. Krishna Murthy Karuturi, Gregory W. Carter, The Alzheimer's Disease Neuroimaging Initiative
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
Subjects:
Alzheimer's disease
convolutional neural networks
deep learning
disease progression
imaging phenotypes
machine learning
Online Access:https://doi.org/10.1002/dad2.12140

Internet

https://doi.org/10.1002/dad2.12140

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