Semi-supervised hierarchical multimodal feature and sample selection for Alzheimer’s disease diagnosis

Le An, Ehsan Adeli, Mingxia Liu, Jun Zhang, Dinggang Shen

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

7 Citations (Scopus)

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that impairs a patient’s memory and other important mental functions. In this paper,we leverage the mutually informative and complementary features from both structural magnetic resonance imaging (MRI) and single nucleotide polymorphism (SNP) for improving the diagnosis. Due to the feature redundancy and sample outliers,direct use of all training data may lead to suboptimal performance in classification. In addition,as redundant features are involved,the most discriminative feature subset may not be identified in a single step,as commonly done in most existing feature selection approaches. Therefore,we formulate a hierarchical multimodal feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps. To positively guide the data manifold preservation,we utilize both labeled and unlabeled data in the learning process,making our method semi-supervised. The finally selected features and samples are then used to train support vector machine (SVM) based classification models. Our method is evaluated on 702 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset,and the superior classification results in AD related diagnosis demonstrate the effectiveness of our approach as compared to other methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages79-87
Number of pages9
Volume9901 LNCS
ISBN (Print)9783319467221
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    An, L., Adeli, E., Liu, M., Zhang, J., & Shen, D. (2016). Semi-supervised hierarchical multimodal feature and sample selection for Alzheimer’s disease diagnosis. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9901 LNCS, pp. 79-87). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_10