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

2.5K
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...
2.5K
Learning Disabilities01:25

Learning Disabilities

585
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
585
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

464
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...
464
Observational Learning01:12

Observational Learning

875
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...
875
Introduction to Learning01:18

Introduction to Learning

1.0K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.0K

You might also read

Related Articles

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

Sort by
Same author

National Mortality Trends Associated with Anomalous Aortic Origin of Coronary Artery in the United States: A CDC WONDER Database Analysis.

Cardiology in review·2026
Same author

Evaluation of anemia in non-enhanced and contrast-enhanced dual-energy CT using electron density imaging.

PloS one·2026
Same author

Where Your Eyes Go: How AI Output Design Impacts Reading Behavior.

Journal of imaging informatics in medicine·2026
Same author

Integrating large language models into radiological practice.

European journal of radiology·2026
Same author

Drug-coated balloon versus drug-eluting stent for isolated ostial side-branch bifurcation (Medina 0.0.1) lesions.

International journal of cardiology·2026
Same author

Sleep Apnoea Variability in Pacemaker Patients: A Women-Predominant Phenotype.

European journal of clinical investigation·2026

Related Experiment Video

Updated: Jan 25, 2026

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

Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning.

Márton Kolossváry1, Carlo N De Cecco2, Gudrun Feuchtner3

  • 1Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary.

Journal of Cardiovascular Computed Tomography
|April 29, 2019
PubMed
Summary

Radiomics, machine learning (ML), and deep learning (DL) extract more data from coronary CT angiography (CCTA) scans than human analysis. These advanced techniques improve the non-invasive assessment of coronary artery disease.

Keywords:
AtherosclerosisCoronary CT angiographyDeep learningMachine learningRadiomics

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.6K

Related Experiment Videos

Last Updated: Jan 25, 2026

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
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.6K

Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Coronary CT angiography (CCTA) applications have expanded due to technical imaging advances.
  • Recent breakthroughs in radiological image post-processing, analysis, and interpretation are enhancing diagnostic capabilities.

Purpose of the Study:

  • To describe the fundamentals of radiomics, machine learning (ML), and deep learning (DL).
  • To highlight the similarities, differences, limitations, and potential pitfalls of these advanced analytical techniques.
  • To provide an overview of recent findings on their application in non-invasive coronary atherosclerosis assessment using CCTA.

Main Methods:

  • Extraction of quantitative features from medical images using radiomics.
  • Analysis of extracted data using machine learning (ML) and deep learning (DL) algorithms.
  • Review of published literature on radiomics, ML, and DL applications in CCTA for coronary atherosclerosis.

Main Results:

  • Radiomics enables extraction of significantly more information from scans compared to visual assessment.
  • ML and DL models, powered by big data and computational advancements, outperform conventional statistical methods.
  • Emerging results show promising applications of these techniques in non-invasive coronary atherosclerosis assessment.

Conclusions:

  • Radiomics, ML, and DL offer powerful tools for precision phenotyping and advanced analysis of medical images.
  • These techniques hold significant potential for improving the non-invasive assessment of coronary artery disease using CCTA.
  • Understanding the nuances and limitations of these methods is crucial for their effective clinical implementation.