Multi-task sparse classifier for diagnosis of MCI conversion to AD with longitudinal MR images

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

4 Citations (Scopus)

Abstract

Mild cognitive impairment (MCI) patients are at a high risk of turning into Alzheimer's disease (AD) within years. But it is known that not all MCI patients will progress to AD. Therefore, it is of great interest to accurately diagnose whether a MCI patient will convert to AD (namely MCI converter; MCI-C) or not (namely MCI non-converter; MCI-NC), for early diagnosis and proper treatment. In this paper, we propose a multi-task sparse representation classifier to discriminate between MCI-C and MCI-NC utilizing longitudinal neuroimaging data. Unlike the previous methods that explicitly combined the longitudinal information in a feature domain, thus requiring the same number of measurements in time, the proposed method is not limited to the availability of the data. Specifically, by means of multi-task learning, we impose a group constraint that the same training samples, ideally belonging to the same class, are used to represent the longitudinal feature vectors across time points. Then we utilize a sparse representation classifier for label decision. From a machine learning perspective, the proposed method can be considered as the combination of the generative and discriminative methods, which are known to be effective in classification enhancement. In our experiments on magnetic resonance brain images of 349 MCI subjects (164 MCI-C and 185 MCI-NC) from ADNI database, we demonstrate the validity of the proposed method, which also outperforms the competing methods.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PublisherSpringer Verlag
Pages243-250
Number of pages8
ISBN (Print)9783319022666
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8184 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/22

Keywords

  • MCI diagnosis
  • longitudinal MR images
  • multi-task sparse learning
  • sparse representation classifier

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, M., Suk, H. I., & Shen, D. (2013). Multi-task sparse classifier for diagnosis of MCI conversion to AD with longitudinal MR images. In Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings (pp. 243-250). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8184 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-02267-3_31