A localized MKL method for brain classification with known intra-class variability

Aydin Ulaş, Mehmet Gönen, Umberto Castellani, Vittorio Murino, Marcella Bellani, Michele Tansella, Paolo Brambilla

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

Abstract

Automatic decisional systems based on pattern classification methods are becoming very important to support medical diagnosis. In general, the overall objective is to classify between healthy subjects and patients affected by a certain disease. To reach this aim, significant efforts have been spent in finding reliable biomarkers which are able to robustly discriminate between the two populations (i.e., patients and controls). However, in real medical scenarios there are many factors, like the gender or the age, which make the source data very heterogeneous. This introduces a large intra-class variation by affecting the performance of the classification procedure. In this paper we exploit how to use the knowledge on heterogeneity factors to improve the classification accuracy. We propose a Clustered Localized Multiple Kernel Learning (CLMKL) algorithm by encoding in the classication model the information on the clusters of apriory known stratifications. Experiments are carried out for brain classification in Schizophrenia. We show that our algorithm performs clearly better than single kernel Support Vector Machines (SVMs), linear MKL algorithms and canonical Localized MKL algorithms when the gender information is considered as apriori knowledge.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages152-159
Number of pages8
Volume7588 LNCS
DOIs
Publication statusPublished - 2012
Event3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France
Duration: Oct 1 2012Oct 1 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7588 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period10/1/1210/1/12

Keywords

  • brain imaging
  • computer-aided diagnosis
  • localized multiple kernel learning
  • magnetic resonance imaging
  • schizophrenia

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Ulaş, A., Gönen, M., Castellani, U., Murino, V., Bellani, M., Tansella, M., & Brambilla, P. (2012). A localized MKL method for brain classification with known intra-class variability. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7588 LNCS, pp. 152-159). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7588 LNCS). https://doi.org/10.1007/978-3-642-35428-1_19