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

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

44.6K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
44.6K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.0K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.0K
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Purposive Learning01:22

Purposive Learning

508
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
508
Observational Learning01:12

Observational Learning

979
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
979

You might also read

Related Articles

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

Sort by
Same author

A Multimodal Acousto-Optic Dataset for Underwater Image Enhancement, Detection, and Reconstruction.

Scientific data·2026
Same author

Functional hierarchy of the human neocortex across the lifespan.

Nature·2026
Same author

A Large-scale Neural Model Inversion Framework for Effective Connectivity Estimation.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

SinoSynth: A Physics-Based Domain Randomization Approach for Generalizable CBCT Image Enhancement.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2025
Same author

Deformation-Aware MR-TRUS Image Translation for Prostate Cancer Brachytherapy.

Research square·2025
Same author

Anatomy-to-tract mapping infers white matter pathways without diffusion streamline propagation.

Nature communications·2025

Related Experiment Video

Updated: Feb 4, 2026

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
11:00

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

5.1K

Learning MRI artefact removal with unpaired data.

Siyuan Liu1, Kim-Han Thung1, Liangqiong Qu1

  • 1Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Nature Machine Intelligence
|February 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for retrospective artefact correction (RAC) using machine learning with unpaired data. This approach effectively removes image artefacts without needing matched corrupted and clean image pairs.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K

Related Experiment Videos

Last Updated: Feb 4, 2026

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
11:00

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

5.1K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Retrospective artefact correction (RAC) enhances medical image quality and usability.
  • Current machine learning (ML) methods for RAC often rely on supervised learning, requiring paired data that is difficult to obtain.
  • The scarcity of paired data limits the practical application of existing ML-based RAC techniques.

Purpose of the Study:

  • To develop and validate a novel RAC neural network that can be trained using unpaired data.
  • To demonstrate the effectiveness of the proposed method in disentangling and removing image artefacts without requiring matched corrupted and artefact-free image pairs.
  • To assess the method's ability to preserve anatomical details while removing artefacts across various image contrasts.

Main Methods:

  • A novel RAC neural network architecture was designed and trained using unpaired image data.
  • The network learns to identify and remove artefacts directly from corrupted images.
  • The method was evaluated on its ability to process images with different contrast properties.

Main Results:

  • The proposed RAC method successfully disentangles and removes unwanted image artefacts.
  • The technique effectively retains crucial anatomical details within the corrected images.
  • Experimental results confirm the method's robustness across diverse image contrasts and artefact types.

Conclusions:

  • Machine learning-based RAC is feasible using unpaired data, overcoming a significant limitation of supervised methods.
  • This approach broadens the applicability of RAC by removing the need for paired training datasets.
  • The developed method offers a promising solution for improving medical image quality and usability in clinical practice.