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

Inhaled Medications01:23

Inhaled Medications

829
Inhaled medications are crucial for managing chronic obstructive pulmonary disease (COPD) and asthma. They are essential for effective treatment and control, ensuring optimal respiratory health and well-being. Inhaled medication delivers drugs directly to the lungs, providing a rapid onset of action and reducing systemic side effects compared to oral or injectable medications. Three primary types of inhalation devices are used to administer these medications: nebulizers, metered-dose inhalers...
829
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
Associative Learning01:27

Associative Learning

1.5K
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...
1.5K
Purposive Learning01:22

Purposive Learning

518
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...
518
Observational Learning01:12

Observational Learning

1.0K
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...
1.0K
Learning Disabilities01:25

Learning Disabilities

634
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...
634

You might also read

Related Articles

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

Sort by
Same author

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same author

SegRap2025: A benchmark of gross tumor volume and lymph node clinical target volume Segmentation for Radiotherapy Planning of nasopharyngeal carcinoma.

Medical image analysis·2026
Same author

Saikosaponin B1 alleviates hepatic fibrosis by targeting the LDHA-MCT1/4 axis to inhibit lactate-driven profibrogenic signaling.

Naunyn-Schmiedeberg's archives of pharmacology·2026
Same author

PromptReg: Interactive Registration by "Corresponding Prompts" for Segment Anything Model (SAM).

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Is Deep Learning Ready for Abdominal Organ-at-Risk Segmentation in the Foundation Model Era: A Comprehensive Study of Challenging Clinical Cases.

International journal of radiation oncology, biology, physics·2026
Same author

In silico modelling of changes in spinal cord blood flow after endovascular aortic aneurysm repair.

Computer methods and programs in biomedicine·2026

Related Experiment Video

Updated: Feb 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.5K

NiftyNet: a deep-learning platform for medical imaging.

Eli Gibson1, Wenqi Li2, Carole Sudre3

  • 1Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.

Computer Methods and Programs in Biomedicine
|March 17, 2018
PubMed
Summary
This summary is machine-generated.

NiftyNet is an open-source platform simplifying deep learning for medical image analysis. It accelerates the development and dissemination of solutions for segmentation, regression, and image generation tasks.

Keywords:
Convolutional neural networkDeep learningGenerative adversarial networkImage regressionMedical image analysisSegmentation

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
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.5K

Related Experiment Videos

Last Updated: Feb 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.5K
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
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

3.5K

Area of Science:

  • Deep learning applications in medical imaging and computer-assisted interventions.
  • Development of specialized deep learning frameworks for healthcare.

Background:

  • Existing deep learning platforms require significant implementation effort for medical image analysis.
  • Lack of a unified infrastructure leads to duplicated efforts and incompatible tools across research groups.

Purpose of the Study:

  • Introduce NiftyNet, an open-source platform designed to streamline deep learning in medical imaging.
  • Facilitate the acceleration and simplification of developing AI-driven medical image analysis solutions.
  • Provide a common platform for sharing and adapting research outputs within the medical imaging community.

Main Methods:

  • NiftyNet offers a modular deep learning pipeline tailored for medical imaging tasks like segmentation, regression, and image generation.
  • The framework integrates components for data loading, augmentation, network architectures, loss functions, and evaluation metrics specific to medical imaging.
  • Built on TensorFlow, NiftyNet supports TensorBoard visualization for 2D and 3D medical images and computational graphs.

Main Results:

  • Demonstrated NiftyNet's utility through three applications: abdominal organ segmentation (CT), CT attenuation map prediction (MRI-to-CT), and simulated ultrasound image generation.
  • Successfully applied the platform to diverse medical imaging analysis tasks, showcasing its versatility.
  • Validated the effectiveness of NiftyNet in developing and deploying specialized medical imaging AI solutions.

Conclusions:

  • NiftyNet empowers researchers to rapidly develop and distribute deep learning solutions for medical image analysis.
  • The platform supports a range of applications including segmentation, regression, and image generation.
  • NiftyNet facilitates the extension of deep learning capabilities to new medical imaging applications.