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

You might also read

Related Articles

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

Sort by
Same author

Autologous transplant versus matched sibling donor transplant in intermediate-risk AML in CR1 with no detectable MRD: a biological assignment comparative study.

Experimental hematology & oncology·2026
Same author

Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge.

Medical image analysis·2026
Same author

Impact of chitosan oligosaccharide on microbiota-metabolite-immune axis in natural aging.

Frontiers in nutrition·2026
Same author

Single-cell profiling reveals reprogrammed hierarchy and disrupted immune-stromal ecosystem in TP53-mutated AML.

Experimental hematology & oncology·2026
Same author

Rainstorm regimes modulate cyanobacterial bloom dynamics in deep reservoirs: Synergistic effects of nutrient pulses and hydrological perturbations.

Limnology and oceanography·2026
Same author

Survival-Informed Multi-Omics Kernel Fusion for Cancer Subtyping.

IEEE transactions on computational biology and bioinformatics·2026
Same journal

Identifying clinical feature clusters toward predicting stroke in patients with asymptomatic carotid stenosis.

International journal of data science and analytics·2025
Same journal

Narratives from GPT-derived networks of news and a link to financial markets dislocations.

International journal of data science and analytics·2025
Same journal

Analyzing international airtime top-up transfers for migration and mobility.

International journal of data science and analytics·2023
Same journal

Evaluating narrative visualization: a survey of practitioners.

International journal of data science and analytics·2023
Same journal

Fake news detection: deep semantic representation with enhanced feature engineering.

International journal of data science and analytics·2023
Same journal

AI and data science for smart emergency, crisis and disaster resilience.

International journal of data science and analytics·2023
See all related articles

Related Experiment Video

Updated: Aug 7, 2025

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

2.8K

Intelligent medical image grouping through interactive learning.

Xuan Guo1, Qi Yu1, Rui Li1

  • 1B. Thomas Golisano College of Computing and Information Sciences, Rochester Institute of Technology, 20 Lomb Memorial Drive, Rochester, NY 14623, USA.

International Journal of Data Science and Analytics
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an interactive machine learning approach for grouping dermatological images. Experts guide the AI, improving image analysis accuracy and efficiency in medical imaging.

Keywords:
Dermatological imagesImage groupingInteractive machine learningMultimodal dataVisual analytics

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:30

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

140
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

8.3K

Related Experiment Videos

Last Updated: Aug 7, 2025

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

2.8K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:30

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

140
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

8.3K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Machine Learning

Background:

  • Image grouping in specialized fields like dermatology is complex, requiring significant human expertise.
  • Traditional machine learning struggles to integrate expert knowledge effectively.
  • Manual annotation of medical images is time-consuming and often insufficient.

Purpose of the Study:

  • To develop an interactive machine learning paradigm for automated, interpretable grouping of dermatological images.
  • To integrate domain expertise directly into the machine learning model's training process.
  • To enhance the accuracy and efficiency of medical image analysis.

Main Methods:

  • An interactive machine learning framework was designed, enabling expert input.
  • Dermatologists provided domain knowledge by grouping a small subset of images.
  • A learning algorithm incorporated expert-defined groupings as constraints for dataset reorganization.

Main Results:

  • The developed paradigm effectively improved image grouping based on expert input.
  • The interactive loop of model computation, visualization, and expert feedback enhanced grouping quality.
  • Demonstrated the feasibility of integrating human expertise into automated image analysis.

Conclusions:

  • Interactive machine learning offers a powerful solution for knowledge-rich image grouping tasks.
  • This approach bridges the gap between computational analysis and expert medical knowledge.
  • The paradigm facilitates more accurate and interpretable dermatological image classification.