Chapter title |
Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model
|
---|---|
Chapter number | 16 |
Book title |
Information Processing in Medical Imaging
|
Published in |
Information processing in medical imaging proceedings of the conference, June 2017
|
DOI | 10.1007/978-3-319-59050-9_16 |
Pubmed ID | |
Book ISBNs |
978-3-31-959049-3, 978-3-31-959050-9
|
Authors |
Xiaoqian Wang, Kefei Liu, Jingwen Yan, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Heng Huang, for the ADNI |
Abstract |
Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among different classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model. |
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