Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model

Xiaoqian Wang, Kefei Liu, Jingwen Yan, Shannon L. Risacher, Andrew Saykin, Li Shen, Heng Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
PublisherSpringer Verlag
Pages198-209
Number of pages12
Volume10265 LNCS
ISBN (Print)9783319590493
DOIs
StatePublished - 2017
Event25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States
Duration: Jun 25 2017Jun 30 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10265 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Conference on Information Processing in Medical Imaging, IPMI 2017
CountryUnited States
CityBoone
Period6/25/176/30/17

Fingerprint

Alzheimer's Disease
Model
Classification Problems
Disorder
Overlap
Experiment
Class

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Wang, X., Liu, K., Yan, J., Risacher, S. L., Saykin, A., Shen, L., & Huang, H. (2017). Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model. In Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings (Vol. 10265 LNCS, pp. 198-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59050-9_16

Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model. / Wang, Xiaoqian; Liu, Kefei; Yan, Jingwen; Risacher, Shannon L.; Saykin, Andrew; Shen, Li; Huang, Heng.

Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Vol. 10265 LNCS Springer Verlag, 2017. p. 198-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10265 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, X, Liu, K, Yan, J, Risacher, SL, Saykin, A, Shen, L & Huang, H 2017, Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model. in Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. vol. 10265 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10265 LNCS, Springer Verlag, pp. 198-209, 25th International Conference on Information Processing in Medical Imaging, IPMI 2017, Boone, United States, 6/25/17. https://doi.org/10.1007/978-3-319-59050-9_16
Wang X, Liu K, Yan J, Risacher SL, Saykin A, Shen L et al. Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model. In Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Vol. 10265 LNCS. Springer Verlag. 2017. p. 198-209. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59050-9_16
Wang, Xiaoqian ; Liu, Kefei ; Yan, Jingwen ; Risacher, Shannon L. ; Saykin, Andrew ; Shen, Li ; Huang, Heng. / Predicting interrelated Alzheimer’s disease outcomes via new self-learned structured low-rank model. Information Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings. Vol. 10265 LNCS Springer Verlag, 2017. pp. 198-209 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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