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Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer’s Disease

Authors :
Lodewijk Brand
Shannon L. Risacher
Heng Huang
Hua Wang
Li Shen
Andrew J. Saykin
Source :
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009274, MICCAI (1)
Publication Year :
2018
Publisher :
Springer International Publishing, 2018.

Abstract

Alzheimer’s disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer’s disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe. By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we propose a new Joint High-Order Multi-Modal Multi-Task Feature Learning method to predict the cognitive performance and diagnosis of patients with and without AD.

Details

ISBN :
978-3-030-00927-4
ISBNs :
9783030009274
Database :
OpenAIRE
Journal :
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009274, MICCAI (1)
Accession number :
edsair.doi.dedup.....3ff74fb4b5da8a1078cc44c4b3800fa0