Learning with heterogeneous data for longitudinal studies

Letizia Squarcina, Cinzia Perlini, Marcella Bellani, Antonio Lasalvia, Mirella Ruggeri, Paolo Brambilla, Umberto Castellani

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

Abstract

Longitudinal studies are very important to understand cerebral structural changes especially during the course of pathologies. For instance, in the context of mental health research, it is interesting to evaluate how a certain disease degenerates over time in order to discriminate between pathological and normal time dependent brain deformations. However longitudinal data are not easily available, and very often they are characterized by a large variability in both the age of subjects and time between acquisitions (follow up time). This leads to heterogeneous data that may affect the overall study. In this paper we propose a learning method to deal with this kind of heterogeneous data by exploiting covariate measures in a Multiple Kernel Learning (MKL) framework. Cortical thickness and white matter volume of the left middle temporal region are collected from each subject. Then, a subject-dependent kernel weighting procedure is introduced in order to obtain the correction of covariate effect simultaneously with classification. Experiments are reported for First Episode Psychosis detection by showing very promising results.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages535-542
Number of pages8
Volume9351
ISBN (Print)9783319245737
DOIs
Publication statusPublished - 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

Keywords

  • First Episode Psychosis
  • Longitudinal study
  • Multiple Kernel Learning
  • Support Vector Machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Squarcina, L., Perlini, C., Bellani, M., Lasalvia, A., Ruggeri, M., Brambilla, P., & Castellani, U. (2015). Learning with heterogeneous data for longitudinal studies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, pp. 535-542). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. https://doi.org/10.1007/978-3-319-24574-4_64