Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Brain Imaging01:14

Brain Imaging

294
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
294
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

49
Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
49

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Copper-mediated amidation of alkenylzirconocenes with acyl azides: formation of enamides.

Organic letters·2013
Same author

JARID1A, JMY, and PTGER4 polymorphisms are related to ankylosing spondylitis in Chinese Han patients: a case-control study.

PloS one·2013
Same author

[The risk factors of ventilator-associated pneumonia in newborn and the changes of isolated pathogens].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2013
Same author

A route to phase controllable Cu2ZnSn(S(1-x)Se(x))4 nanocrystals with tunable energy bands.

Scientific reports·2013
Same author

Efficacy of an infection control program in reducing ventilator-associated pneumonia in a Chinese neonatal intensive care unit.

American journal of infection control·2013
Same author

[Effect of different forms of inorganic nitrogen on the photodegradation of antipyrine in water].

Huan jing ke xue= Huanjing kexue·2013

Related Experiment Video

Updated: Aug 28, 2025

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies
10:09

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

Published on: September 22, 2014

13.3K

Self-supervised learning for modal transfer of brain imaging.

Dapeng Cheng1,2, Chao Chen1, Mao Yanyan1,3

  • 1School of Computer Science and Technology, Shandong Business and Technology University, Yantai, China.

Frontiers in Neuroscience
|September 19, 2022
PubMed
Summary

This study introduces a novel brain imaging technique using self-supervised learning to synthesize missing medical scan data. This method enhances diagnostic accuracy by leveraging multimodal data diversity.

Keywords:
auxiliary tasksbrain imaginggenerative adversarial networkmultiple modalself-supervised learning

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.3K

Related Experiment Videos

Last Updated: Aug 28, 2025

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies
10:09

Transferring Cognitive Tasks Between Brain Imaging Modalities: Implications for Task Design and Results Interpretation in fMRI Studies

Published on: September 22, 2014

13.3K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.6K
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

48.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Current brain imaging techniques often involve transferring data between modalities.
  • Synthesizing missing data from multimodal sources can improve clinical diagnosis.
  • Existing methods may not fully exploit the diversity within multimodal brain imaging data.

Purpose of the Study:

  • To introduce a self-supervised learning framework for brain imaging modality transfer.
  • To develop a method for synthesizing missing modal data by leveraging multimodal data diversity.
  • To improve the utility of brain imaging data for clinical diagnosis and downstream tasks.

Main Methods:

  • Developed a self-supervised learning cycle-consistent generative adversarial network (BSL-GAN).
  • Employed a multi-branch input structure to learn multimodal data characteristics.
  • Utilized auxiliary tasks on large-scale unsupervised data to mine supervision information.

Main Results:

  • The BSL-GAN framework effectively transfers data between brain imaging modalities.
  • The method ensures similarity between input and output modal images.
  • Learned representations are valuable for downstream analytical tasks.

Conclusions:

  • The proposed BSL-GAN offers a powerful approach for brain imaging modality transfer.
  • Self-supervised learning effectively synthesizes missing multimodal data.
  • This technique has the potential to enhance clinical diagnosis and neuroimaging research.