Max-margin based learning for discriminative Bayesian network from neuroimaging data.

Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Recently, neuroimaging data have been increasingly used to study the causal relationship among brain regions for the understanding and diagnosis of brain diseases. Recent work on sparse Gaussian Bayesian network (SGBN) has shown it as an efficient tool to learn large scale directional brain networks from neuroimaging data. In this paper, we propose a learning approach to constructing SGBNs that are both representative and discriminative for groups in comparison. A max-margin criterion built directly upon the SGBN models is proposed to effectively optimize the classification performance of the SGBNs. The proposed method shows significant improvements over the state-of-the-art works in the discriminative power of SGBNs.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages321-328
Number of pages8
Volume17
EditionPt 3
Publication statusPublished - 2014 Jan 1

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Neuroimaging
Learning
Brain
Brain Diseases
Power (Psychology)

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zhou, L., Wang, L., Liu, L., Ogunbona, P., & Shen, D. (2014). Max-margin based learning for discriminative Bayesian network from neuroimaging data. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 17, pp. 321-328)

Max-margin based learning for discriminative Bayesian network from neuroimaging data. / Zhou, Luping; Wang, Lei; Liu, Lingqiao; Ogunbona, Philip; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 17 Pt 3. ed. 2014. p. 321-328.

Research output: Chapter in Book/Report/Conference proceedingChapter

Zhou, L, Wang, L, Liu, L, Ogunbona, P & Shen, D 2014, Max-margin based learning for discriminative Bayesian network from neuroimaging data. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 17, pp. 321-328.
Zhou L, Wang L, Liu L, Ogunbona P, Shen D. Max-margin based learning for discriminative Bayesian network from neuroimaging data. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 17. 2014. p. 321-328
Zhou, Luping ; Wang, Lei ; Liu, Lingqiao ; Ogunbona, Philip ; Shen, Dinggang. / Max-margin based learning for discriminative Bayesian network from neuroimaging data. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 17 Pt 3. ed. 2014. pp. 321-328
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