Sparse multimodal manifold-regularized transfer learning for MCI conversion prediction

Bo Cheng, Daoqiang Zhang, Biao Jie, Dinggang Shen

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

3 Citations (Scopus)

Abstract

Effective prediction of conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for early diagnosis of AD, as well as for evaluating AD risk pre-symptomatically. Different from most traditional methods for MCI conversion prediction, in this paper, we propose a novel sparse multimodal manifold-regularized transfer learning classification (SM2TLC) method, which can simultaneously use other related classification tasks (e.g., AD vs. normal controls (NC) classification) and also the unlabeled data for improving the MCI conversion prediction. Our proposed method includes two key components: (1) a criterion based on the maximum mean discrepancy (MMD) for eliminating the negative effect related to the distribution differences between the auxiliary (i.e., AD/NC) and the target (i.e., MCI converters/MCI non-converters) domains, and (2) a sparse semisupervised manifold-regularized least squares classification method for utilization of unlabeled data. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that the proposed method can significantly improve the classification performance between MCI converters and MCI non-converters, compared with the state-of-the-art methods.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages251-259
Number of pages9
Volume8184 LNCS
ISBN (Print)9783319022666
DOIs
Publication statusPublished - 2013 Jan 1
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)03029743
ISSN (Electronic)16113349

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

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Cheng, B., Zhang, D., Jie, B., & Shen, D. (2013). Sparse multimodal manifold-regularized transfer learning for MCI conversion prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8184 LNCS, pp. 251-259). (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_32