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

Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K

You might also read

Related Articles

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

Sort by
Same author

In vivo dedifferentiation of human epidermal cells.

Cell biology international·2007
Same author

What is in a word? No versus Yes differentially engage the lateral orbitofrontal cortex.

Emotion (Washington, D.C.)·2007
Same author

[Gene expression profile changes in oral verrucous carcinoma and oral squamous cell carcinoma].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2007
Same author

Morphology of critical nuclei in solid-state phase transformations.

Physical review letters·2007
Same author

Enhanced cooperative activation effect in the hydrolytic kinetic resolution of epoxides on [Co(salen)] catalysts confined in nanocages.

Angewandte Chemie (International ed. in English)·2007
Same author

[Construction of the three-dimensional finite element model of micro -implant -maxilla].

Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology·2007

Related Experiment Video

Updated: Oct 18, 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

WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification.

Yan Wang, Zizhou Wang, Yangqin Feng

    IEEE Transactions on Medical Imaging
    |October 4, 2021
    PubMed
    Summary

    This study introduces a novel deep learning approach for improved mammographic image classification, enhancing early breast cancer detection. The new method effectively learns discriminative features, outperforming existing techniques on benchmark datasets.

    More Related Videos

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.0K
    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

    Related Experiment Videos

    Last Updated: Oct 18, 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
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    7.0K
    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

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Early breast cancer detection significantly improves patient outcomes.
    • Mammography is a key screening tool, but radiologist workload can be high.
    • Current deep learning models using softmax loss struggle to learn discriminative features from complex mammographic data.

    Purpose of the Study:

    • To develop an automatic mammographic image classification method to enhance radiologist efficiency.
    • To improve the feature extraction capabilities of deep learning models for mammograms.
    • To enhance the detection of difficult-to-classify breast cancer cases.

    Main Methods:

    • Designed a double-classifier network architecture to constrain feature distribution via modified decision boundaries.
    • Proposed a double-classifier constraint loss function to learn more discriminative features.
    • Introduced a weighted double-classifier constraint to focus on challenging samples.

    Main Results:

    • The proposed method significantly improved mammographic image classification performance.
    • Outperformed existing and state-of-the-art methods on three public benchmark datasets.
    • Demonstrated the effectiveness of the double-classifier architecture and novel loss functions.

    Conclusions:

    • The developed double-classifier network and loss functions effectively learn discriminative features for mammographic images.
    • This approach enhances the accuracy of automated breast cancer detection systems.
    • The method is easily applicable to existing convolutional neural networks, offering a practical improvement for clinical workflows.