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

12 Citations (Scopus)


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
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467221
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)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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