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

X-ray Imaging01:24

X-ray Imaging

7.7K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
7.7K

You might also read

Related Articles

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

Sort by
Same author

Radiomics in Medical Imaging: Methods, Applications, and Challenges.

Journal of imaging·2026
Same author

An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables.

Journal of computational social science·2023
Same author

Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach.

Journal of computational social science·2021
Same author

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: May 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

A Survey on Explainable Artificial Intelligence (XAI) Techniques for Visualizing Deep Learning Models in Medical

Deepshikha Bhati1, Fnu Neha1, Md Amiruzzaman2

  • 1Department of Computer Science, Kent State University, Kent, OH 44242, USA.

Journal of Imaging
|October 25, 2024
PubMed
Summary

Deep learning in medical imaging aids diagnosis but lacks transparency. This survey explores interpretability and visualization techniques to understand these complex models, enhancing trust and clinical use.

Keywords:
deep learningexplainable AImachine learningmedical imagingmodel interpretability

More Related Videos

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

2.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

370

Related Experiment Videos

Last Updated: May 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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

2.7K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

370

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Deep learning models significantly advance medical imaging diagnostics and prognostics.
  • The complexity of deep learning models creates a 'black-box' problem, hindering understanding of their decision-making.
  • Interpretability and visualization are essential for trusting AI in healthcare.

Purpose of the Study:

  • To survey and analyze interpretation and visualization techniques for deep learning in medical imaging.
  • To provide insights into the methodologies, applications, and effectiveness of these techniques.
  • To enhance the reliability and clinical relevance of AI-driven medical image analysis.

Main Methods:

  • Comprehensive literature review of interpretability and visualization methods.
  • Categorization and analysis of existing techniques.
  • Evaluation of techniques based on their ability to explain deep learning models.

Main Results:

  • Identification of diverse interpretation and visualization strategies.
  • Discussion of the strengths and limitations of various approaches.
  • Assessment of how these techniques improve model transparency and trustworthiness.

Conclusions:

  • Interpretability and visualization are critical for the adoption of deep learning in clinical practice.
  • Effective techniques can bridge the gap between AI capabilities and clinical needs.
  • Further research can optimize these methods for enhanced medical AI reliability.