Diagnosis of Alzheimer’s disease using view-aligned hypergraph learning with incomplete multi-modality data

Mingxia Liu, Jun Zhang, Pew Thian Yap, Dinggang Shen

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

    12 Citations (Scopus)

    Abstract

    Effectively utilizing incomplete multi-modality data for diagnosis of Alzheimer’s disease (AD) is still an area of active research. Several multi-view learning methods have recently been developed to deal with missing data,with each view corresponding to a specific modality or a combination of several modalities. However,existing methods usually ignore the underlying coherence among views,which may lead to suboptimal learning performance. In this paper,we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among the views. Specifically,we first divide the original data into several views based on possible combinations of modalities,followed by a sparse representation based hypergraph construction process in each view. A view-aligned hypergraph classification (VAHC) model is then proposed,by using a view-aligned regularizer to model the view coherence. We further assemble the class probability scores generated from VAHC via a multi-view label fusion method to make a final classification decision. We evaluate our method on the baseline ADNI-1 database having 807 subjects and three modalities (i.e.,MRI,PET,and CSF). Our method achieves at least a 4.6% improvement in classification accuracy compared with state-of-the-art methods for AD/MCI diagnosis.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
    EditorsSebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
    PublisherSpringer Verlag
    Pages308-316
    Number of pages9
    ISBN (Print)9783319467191
    DOIs
    Publication statusPublished - 2016
    Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
    Duration: 2016 Oct 212016 Oct 21

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9900 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
    Country/TerritoryGreece
    CityAthens
    Period16/10/2116/10/21

    Bibliographical note

    Funding Information:
    D. Shen—This study was supported in part by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, AG030514, and NS093842).

    Publisher Copyright:
    © Springer International Publishing AG 2016.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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