Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective

Guray Erus, Mohamad Habes, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

Neuroimaging data is both very large and complex, as it encompasses many dimensions related to structure, function, and pathology. A growing interest in neuroimaging research has been focused on the use of machine learning methods for the analysis of complex brain imaging data. The success of machine learning approaches depends greatly on the existence of large-scale studies that are complemented with a broad and deep phenotyping. In recent years, studies started to collect imaging data with large samples and mega-analyses started to emerge by pooling thousands of samples together. These studies present opportunities, as well as new challenges, for neuroimaging research, enabling the application of techniques that typically require very large sample sizes for model training and advanced and exploratory analytic techniques that go beyond classical machine learning methods. We present a multifaceted sample of these methods and studies involving machine learning principles applied to large scale population studies.

Original languageEnglish (US)
Title of host publicationHandbook of Medical Image Computing and Computer Assisted Intervention
PublisherElsevier
Pages379-399
Number of pages21
ISBN (Electronic)9780128161760
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Keywords

  • Biomarkers
  • Deep learning
  • Large scale studies
  • Machine learning
  • Neuroimaging
  • SVM

ASJC Scopus subject areas

  • Computer Science(all)

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