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

6 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 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)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
CountryJapan
CityNagoya
Period13/9/2213/9/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Jie, B., Zhang, D., Suk, H. I., Wee, C. Y., & Shen, D. (2013). Integrating multiple network properties for MCI identification. In Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings (pp. 9-16). (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_2