Deep neural networks

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)

Abstract

Deep learning (DL) is a family of machine learning methods capable of detecting multiple levels of latent representations from the data. This is achieved by combining consecutive layers of simple nonlinear transformations that allow the extraction of increasingly abstract features. DL has become one of the most popular and promising approaches in machine learning. In this chapter, we introduce the fundamentals of DL by focusing on the architecture and training process of a typical model: the deep neural network (DNN). We follow this by discussing the main challenges in its application to brain disorders. Finally, we review some exemplary implementations of DNNs to diagnostic and prognostic studies in clinical neuroimaging. Overall, although there are critical challenges to overcome, the use of DL has shown encouraging results. With the increasing availability of larger sample sizes, its application to brain disorders is likely to continue to expand in the next years.
Original languageEnglish
Title of host publicationMachine Learning
Subtitle of host publicationMethods and Applications to Brain Disorders
PublisherElsevier
Pages157-172
Number of pages16
ISBN (Electronic)9780128157398
ISBN (Print)9780128157398
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Alzheimer’s disease
  • Autism spectrum disorder
  • Brain disorders
  • Deep learning
  • Deep neural network
  • Machine learning
  • Neuroimaging
  • Representation learning
  • Schizophrenia

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