Learning from enhanced contextual similarity in brain imaging data for classification of schizophrenia

Tewodros Mulugeta Dagnew, Letizia Squarcina, Massimo W. Rivolta, Paolo Brambilla, Roberto Sassi

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

Abstract

In certain severe mental diseases, like schizophrenia, structural alterations of the brain are detectable by magnetic resonance imaging (MRI). In this work, we try to automatically distinguish, by using anatomical features obtained from MRI images, schizophrenia patients from healthy controls. We do so by exploiting contextual similarity of imaging data, enhanced with a distance metric learning strategy (DML - by providing “must-be-in-the-same-class” and “must-not-be-in-the-same-class” pairs of subjects). To learn from contextual similarity of the subjects brain anatomy, we use a graph-based semi-supervised label propagation algorithm (graph transduction, GT) and compare it to standard supervised techniques (SVM and K-nearest neighbor, KNN). We performed out tests on a population of 20 schizophrenia patients and 20 healthy controls. DML+GT achieved a statistically significant advantage in classification performance (Accuracy: 0.74, Sensitivity: 0.79, Specificity: 0.69, Ck: 0.48). Enhanced contextual similarity improved performance of GT, SVM and KNN offering promising perspectives for MRI images analysis.

Original languageEnglish
Title of host publicationImage Analysis and Processing - ICIAP 2017 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages265-275
Number of pages11
Volume10484 LNCS
ISBN (Print)9783319685595
DOIs
Publication statusPublished - Jan 1 2017
Event19th International Conference on Image Analysis and Processing, ICIAP 2017 - Catania, Italy
Duration: Sep 11 2017Sep 15 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10484 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th International Conference on Image Analysis and Processing, ICIAP 2017
CountryItaly
CityCatania
Period9/11/179/15/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Dagnew, T. M., Squarcina, L., Rivolta, M. W., Brambilla, P., & Sassi, R. (2017). Learning from enhanced contextual similarity in brain imaging data for classification of schizophrenia. In Image Analysis and Processing - ICIAP 2017 - 19th International Conference, Proceedings (Vol. 10484 LNCS, pp. 265-275). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10484 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-68560-1_24