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2017 | OriginalPaper | Buchkapitel

Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model

verfasst von : Xiaoqian Wang, Kefei Liu, Jingwen Yan, Shannon L. Risacher, Andrew J. Saykin, Li Shen, Heng Huang, for the ADNI

Erschienen in: Information Processing in Medical Imaging

Verlag: Springer International Publishing

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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 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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Vorheriges Kapitel Multi-source Multi-target Dictionary Learning for Prediction of Cognitive Decline

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Titel: Predicting Interrelated Alzheimer’s Disease Outcomes via New Self-learned Structured Low-Rank Model
verfasst von: Xiaoqian Wang
Kefei Liu
Jingwen Yan
Shannon L. Risacher
Andrew J. Saykin
Li Shen
Heng Huang
for the ADNI
Verlag: Springer International Publishing
Buch: Information Processing in Medical Imaging
Print ISBN: 978-3-319-59049-3

Electronic ISBN: 978-3-319-59050-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59050-9_16

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