English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Using GAN for learning joint task/response distribution in fMRI

Lee, J., Loktyushin, A., Stelzer, J., & Lohmann, G. (submitted). Using GAN for learning joint task/response distribution in fMRI.

Item is

Basic

show hide
Genre: Meeting Abstract

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Lee, JY1, 2, Author              
Loktyushin, A1, 2, Author              
Stelzer, J1, 2, Author              
Lohmann, G1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: This is a proof-of-principle study on using generative adversarial network (GAN) to synthesize functional Magnetic Resonance Imaging (fMRI) data. We trained GAN to model the joint distribution of motor task functional magnetic resonance imaging (fMRI) data and the corresponding task labels. Synthesized images by the trained GAN successfully replicated the task relevant fMRI signal in the motor cortex. This result shows a potential for using GAN to augment fMRI data.

Details

show
hide
Language(s):
 Dates: 2019-05
 Publication Status: Submitted
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

Event

show
hide
Title: Medical Imaging with Deep Learning (MIDL 2019)
Place of Event: London, UK
Start-/End Date: 2019-07-08 - 2019-07-10

Legal Case

show

Project information

show

Source 1

show
hide
Title: Medical Imaging with Deep Learning (MIDL 2019)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -