Structural feature selection for connectivity network-based MCI diagnosis

Biao Jie, Daoqiang Zhang, Chong Yaw Wee, Dinggang Shen

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

2 Citations (Scopus)

Abstract

Connectivity networks have been recently used for classification of neurodegenerative diseases, e.g., mild cognitive impairment (MCI). In typical connectivity network-based classification, features are often extracted from (multiple) connectivity networks and concatenated into a long vector for subsequent feature selection and classification. However, some useful network topological information may be lost in this type of approach. In this paper, we propose a new structural feature selection method which embeds the topological information of connectivity networks through graph kernel and then uses recursive feature elimination with graph kernel (RFE-GK) to select the most discriminative features. Furthermore, multiple kernel learning (MKL) is also adopted to combine multiple graph kernels for joint structural feature selectionfrom multiple connectivity networks. The experimental results show the efficacy of our proposed method with comparison to the state-of-the-art method in MCI classification, based on the connectivity networks.

Original languageEnglish
Title of host publicationMultimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
Pages175-184
Number of pages10
DOIs
Publication statusPublished - 2012
Event2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 2012 Oct 12012 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7509 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period12/10/112/10/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Structural feature selection for connectivity network-based MCI diagnosis'. Together they form a unique fingerprint.

  • Cite this

    Jie, B., Zhang, D., Wee, C. Y., & Shen, D. (2012). Structural feature selection for connectivity network-based MCI diagnosis. In Multimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings (pp. 175-184). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7509 LNCS). https://doi.org/10.1007/978-3-642-33530-3_15