A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis release_64ze55onzfemhgpebvsewe3fki

by Li Zhang and Mingliang Wang and Mingxia Liu and Daoqiang Zhang

Released as a article .

2020  

Abstract

Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures, by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.
In text/plain format

Archived Files and Locations

application/pdf  904.1 kB
file_2hb4xem545f7bcnbzo3wbk7o64
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2020-05-10
Version   v1
Language   en ?
arXiv  2005.04573v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 4a8b2da3-b3a3-4e83-acb7-687e461c1f60
API URL: JSON