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 Experiment Video

Updated: Jul 11, 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

Automatic image modality based classification and annotation to improve medical image retrieval.

Jayashree Kalpathy-Cramer1, William Hersh

  • 1Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA.

Studies in Health Technology and Informatics
|October 4, 2007
PubMed
Summary
This summary is machine-generated.

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

Domain Generalization Mitigates Scanner-Induced Domain Shift in Medical Imaging.

Journal of imaging informatics in medicine·2026
Same author

Application of a Quantitative Vascular Severity Score in Retinopathy of Prematurity in the United States and India: New Insights Into Disease Epidemiology and Pathophysiology.

American journal of ophthalmology·2026
Same author

Joint Neural Network for Fast Retrospective Rigid Motion Correction of Accelerated Segmented Multislice MRI.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Clinical document metadata extraction: A scoping review.

Journal of biomedical informatics·2026
Same author

Latent Supervision: A Method for Improved Performance and Calibration of Machine Learning Classification Models in Ophthalmology.

Ophthalmology science·2026
Same author

Leveraging deep learning to infer continuous predictions from ordinal labels in medical imaging.

PLOS digital health·2026

An automatic classifier for medical image modality was developed, achieving over 95% accuracy for both grayscale and color images. This tool enhances medical image retrieval by improving search result precision.

Area of Science:

  • Medical Informatics
  • Computer Vision

Background:

  • Medical image retrieval is crucial for diagnostics and education.
  • Image modality is a key characteristic for improving retrieval, but often missing in datasets.
  • Existing systems lack automated modality classification.

Purpose of the Study:

  • To develop and evaluate automatic classifiers for medical image modality.
  • To improve the performance of medical image retrieval systems.

Main Methods:

  • Developed neural network-based classifiers for grayscale and color medical images.
  • Extracted low-level color and texture features for network training.
  • Evaluated classifiers on CISMeF and ImageCLEFmed 2006 databases.

Main Results:

Related Experiment Videos

Last Updated: Jul 11, 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

  • Achieved >95% accuracy for both grayscale and color image modality classifiers.
  • Demonstrated improved precision in medical image retrieval when using modality classification to re-sort textual query results.

Conclusions:

  • Automatic medical image modality classification is feasible and highly accurate.
  • Modality classification can significantly enhance the precision of medical image retrieval systems.