Deep ensemble learning of sparse regression models for brain disease diagnosis
Year: 2017
Type: article
Source: Medical Image Analysis
Authors: Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
Institutions: Korea University, University of North Carolina at Chapel Hill
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: Alzheimer's Disease Neuroimaging Initiative, artsACT, National Research Foundation, Ministry of Science, ICT and Future Planning, National Research Foundation of Korea, Ministry of Science ICT and Future Planning, National Institutes of Health, Institute for Information and Communications Technology Promotion, National Institute on Aging, National Institute of Mental Health, National Institute of Biomedical Imaging and Bioengineering
Awards: G1320617785, G1341012151, G159365751, G1869414055, G2248288783, Analyzing Large-Scale Neuroimaging Data in Alzheimer's Disease, Infant Brain Measurement and Super-Resolution Atlas Construction, G306824652, G3738268348, G3766854581, G4279560943, G4289578186, Assessing Large-scale Brain Connectivities in Mild Cognitive Impairment, Quantifying Brain Abnormality by Multimodality Neuroimage Analysis, Diagnosis of Alzheimer's Disease Using Dynamic High-Order Brain Networks, G5085154924, Longitudinal Mapping of Human Brain Development in the First Years of Life, G580372380, G5874234525, Imaging Genomics Based Brain Disease Prediction, Constructing 4-dimensional Infant Cortical Surface Atlases, G718768636, Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI, G764332481, G7753393047, G8636094558, G8854359148, G8976585520, G934781465
OpenAlex ID: W2582180708