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2018 | OriginalPaper | Chapter

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

Authors : Lodewijk Brand, Hua Wang, Heng Huang, Shannon Risacher, Andrew Saykin, Li Shen, for the ADNI

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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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.

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Title: Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer’s Disease
Authors: Lodewijk Brand
Hua Wang
Heng Huang
Shannon Risacher
Andrew Saykin
Li Shen
for the ADNI
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00927-4

Electronic ISBN: 978-3-030-00928-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00928-1_63

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