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: Sep 24, 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

3.0K

EVALUATION OF COMPLEXITY MEASURES FOR DEEP LEARNING GENERALIZATION IN MEDICAL IMAGE ANALYSIS.

Aleksandar Vakanski1, Min Xian2

  • 1Department of Nuclear Engineering and Industrial Management, University of Idaho, Idaho Falls, USA.

IEEE International Workshop on Machine Learning for Signal Processing : [Proceedings]. IEEE International Workshop on Machine Learning for Signal Processing
|May 9, 2022
PubMed
Summary

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

Compilation of Creep Property Data for Nuclear Structural Materials.

Scientific data·2026
Same author

Machine learning-based correlation of charpy impact properties between sub-sized and standard-sized specimens for nuclear structural materials.

Scientific reports·2026
Same author

Failure Event Mining With Fine-Tuned Large Language Model: Case Study of Analyzing United States Nuclear Power Plant Failure Event Reports.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

From Capture-Recapture to No Recapture: Efficient SCAD Even After Software Updates.

Sensors (Basel, Switzerland)·2026
Same author

GCSAM: Gradient Centralized Sharpness Aware Minimization.

IEEE access : practical innovations, open solutions·2025
Same author

Automated Skin Cancer Report Generation via a Knowledge-Distilled Vision-Language Model.

IEEE access : practical innovations, open solutions·2025

Deep learning models for medical imaging struggle with generalization across different data sources. PAC-Bayes flatness and path norm measures best predict generalization for breast ultrasound classifiers.

Area of Science:

  • Medical image analysis
  • Deep learning
  • Generalization capacity

Background:

  • Deep learning models in medical imaging often exhibit poor generalization on data from different sources (e.g., devices, patient populations).
  • Accurate assessment of generalization is vital for clinical trust, but current complexity measures and generalization bounds show discrepancies with actual performance.
  • Existing empirical studies often use general-purpose image datasets, limiting their applicability to specific medical domains.

Purpose of the Study:

  • To empirically investigate the correlation between 25 complexity measures and the generalization abilities of deep learning classifiers for breast ultrasound images.
  • To identify which complexity measures are most effective in predicting generalization performance in this specific medical context.
  • To evaluate the impact of a multi-task approach (classification and segmentation) on generalization.
Keywords:
Complexity MeasuresDeep LearningGeneralizationMedical Image Analysis

Related Experiment Videos

Last Updated: Sep 24, 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

3.0K

Main Methods:

  • An empirical study was conducted using deep learning classifiers trained on breast ultrasound images.
  • 25 different complexity measures were calculated for the trained models.
  • The correlation between these complexity measures and the models' generalization performance on unseen data was analyzed.

Main Results:

  • PAC-Bayes flatness and path norm measures demonstrated the most consistent correlation with generalization performance across different model-data combinations.
  • These specific complexity measures offer a more reliable prediction of generalization compared to others evaluated.
  • Employing a multi-task learning approach, combining classification and segmentation, was found to enhance generalization capabilities.

Conclusions:

  • PAC-Bayes flatness and path norm are promising metrics for assessing and predicting the generalization of deep learning models in breast ultrasound analysis.
  • The findings suggest that these measures can help improve the trustworthiness of AI in medical imaging by providing better estimates of real-world performance.
  • Multi-task learning strategies, integrating segmentation with classification, can be effectively leveraged to improve the generalization of deep learning models for breast ultrasound applications.