Integrating multiple network properties for MCI identification

Biao Jie, Daoqiang Zhang, Heung Il Suk, Chong Yaw Wee, Dinggang Shen

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

    9 Citations (Scopus)

    Abstract

    Recently, machine learning techniques have been actively applied to the identification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the existing methods focus on using only single network property, although combination of multiple network properties such as local connectivity and topological properties may be more powerful. Employing the kernel-based method, we propose a novel classification framework that attempts to integrate multiple network properties for improving the MCI classification. Specifically, two different types of kernel (i.e., vector-kernel and graph-kernel) extracted from multiple sub-networks are used to quantify two different yet complementary network properties. A multi-kernel learning technique is further adopted to fuse these heterogeneous kernels for MCI classification. Experimental results show that the proposed multiple-network- properties based method outperforms conventional single-network-property based 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
    Pages9-16
    Number of pages8
    ISBN (Print)9783319022666
    DOIs
    Publication statusPublished - 2013
    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 Sept 222013 Sept 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
    Country/TerritoryJapan
    CityNagoya
    Period13/9/2213/9/22

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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