Applications of generative adversarial networks in neuroimaging and clinical neuroscience.
Details
Download: Applications of generative adversarial networks in neuroimaging and clinical neuroscience.pdf (4836.49 [Ko])
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_502D4DD8959F
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
Applications of generative adversarial networks in neuroimaging and clinical neuroscience.
Journal
NeuroImage
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Publication state
Published
Issued date
01/04/2023
Peer-reviewed
Oui
Volume
269
Pages
119898
Language
english
Notes
Publication types: Journal Article ; Review ; Research Support, N.I.H., Extramural
Publication Status: ppublish
Publication Status: ppublish
Abstract
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.
Keywords
Humans, Neuroimaging, Neurosciences, Aging, Alzheimer Disease, Brain, GAN, Generative adversarial network, Pathology, Review
Pubmed
Web of science
Open Access
Yes
Create date
10/02/2023 16:39
Last modification date
25/06/2024 6:29