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

Practical pathways for evaluating artificial intelligence in intracranial aneurysm detection.

Science bulletin·2026
Same author

Development and validation of A CCTA-based risk prediction model for major adverse cardiovascular events in esophageal cancer patients receiving radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026
Same author

Deep Learning for Coronary Stenosis Detection in Heavily Calcified Plaques at Coronary CT Angiography: A Stepwise, Multicenter Study.

Radiology. Artificial intelligence·2025
Same author

Multiparametric MRI deep learning model based on dynamic Contrast-enhanced and apparent diffusion coefficient map enables accurate prediction of benign and malignant breast lesions.

Cancer imaging : the official publication of the International Cancer Imaging Society·2025
Same author

A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial.

Nature communications·2025
Same author

Targeting HDAC8 sensitizes tyrosine kinase inhibitors in the elimination of B-cell acute lymphoblastic leukemia cells through degradation of HIF-1α.

Leukemia·2025

Related Experiment Video

Updated: Oct 19, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.1K

Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification.

Churan Wang, Jing Li, Fandong Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 17, 2021
    PubMed
    Summary

    This study introduces a novel counterfactual generative network for mammogram analysis. The method effectively identifies lesion areas using a symmetric prior, improving benign or malignant classification without explicit lesion annotations.

    More Related Videos

    Endoscopic Bilateral Nipple-sparing Mastectomy via a Single Axillary Incision with Immediate Pre-pectoral Implant-based Breast Reconstruction
    13:35

    Endoscopic Bilateral Nipple-sparing Mastectomy via a Single Axillary Incision with Immediate Pre-pectoral Implant-based Breast Reconstruction

    Published on: May 17, 2024

    3.2K
    Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
    08:32

    Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

    Published on: October 2, 2020

    6.5K

    Related Experiment Videos

    Last Updated: Oct 19, 2025

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.1K
    Endoscopic Bilateral Nipple-sparing Mastectomy via a Single Axillary Incision with Immediate Pre-pectoral Implant-based Breast Reconstruction
    13:35

    Endoscopic Bilateral Nipple-sparing Mastectomy via a Single Axillary Incision with Immediate Pre-pectoral Implant-based Breast Reconstruction

    Published on: May 17, 2024

    3.2K
    Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model
    08:32

    Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

    Published on: October 2, 2020

    6.5K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Classifying mammograms as benign or malignant is difficult without precise lesion annotations.
    • Existing methods struggle with image-level labels, limiting diagnostic accuracy.

    Purpose of the Study:

    • To develop a method for lesion area identification in mammograms using only image-level labels.
    • To improve benign or malignant classification by leveraging a symmetric prior between bilateral breast images.

    Main Methods:

    • A causal model based on a symmetric prior for bilateral mammograms was developed.
    • A counterfactual generative network, integrating a Generative Adversarial Network and a prediction feedback mechanism, was proposed.
    • Optimization focused on distributional distances between counterfactual and actual features in lesion and lesion-free areas.

    Main Results:

    • The counterfactual generative network successfully identified lesion areas, enhancing classification performance.
    • The proposed method achieved state-of-the-art results on the INBreast dataset and an in-house dataset.
    • Ablation studies confirmed the effectiveness of individual modules within the model.

    Conclusions:

    • The developed counterfactual approach offers a promising solution for mammogram classification with limited annotations.
    • This method effectively utilizes bilateral image symmetry to infer lesion locations and improve diagnostic accuracy.
    • The integration of generative networks and classification feedback provides a robust framework for medical image analysis.