Transductive maximum margin classification of ADHD using resting state fMRI

Lei Wang, Danping Li, Tiancheng He, Stephen T.C. Wong, Zhong Xue

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

6 Scopus citations

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) provides key neural imaging characteristics for quantitative assessment and better understanding of the mechanisms of attention deficit hyperactivity disorder (ADHD). Recent multivariate analysis studies showed that functional connectivity (FC) could be used to classify ADHD from normal controls at the individual level. However, there may not be sufficient large numbers of labeled training samples for a hand-on classifier especially for disease classification. In this paper, we propose a transductive maximum margin classification (TMMC) method that uses the available unlabeled data in the learning process. On one hand, the maximum margin classification (MMC) criterion is used to maximize the class margin for the labeled data; on the other hand, a smoothness constraint is imposed on both labeled and unlabeled data projection so that similar samples tend to share the same label. To evaluate the performance of TMMC, experiments on a benchmark cohort from the ADHD-200 competition were performed. The results show that TMMC can improve the performance of ADHD classification using rs-fMRI by involving unlabeled samples, even for small number of labeled training data.

Original languageEnglish (US)
Title of host publicationMachine Learning in Medical Imaging - 7th International Workshop, MLMI 2016 held in conjunction with MICCAI 2016, Proceedings
EditorsLi Wang, Heung-Il Suk, Yinghuan Shi, Ehsan Adeli, Qian Wang
PublisherSpringer-Verlag
Pages221-228
Number of pages8
ISBN (Print)9783319471563
DOIs
StatePublished - 2016
Event7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: Oct 17 2016Oct 17 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10019 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th International Workshop on Machine Learning in Medical Imaging, MLMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
CountryGreece
CityAthens
Period10/17/1610/17/16

Keywords

  • ADHD classification
  • Maximum margin classification
  • Rs-fMRI
  • Transductive learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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