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Related Experiment Video

Updated: May 3, 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

3.6K

A deep learning framework for accurate mammographic mass classification using local context attention module.

Ibrahim Abdelhalim1, Yassir Almalki2,3, Abdelrahman Abdallah4

  • 1Department of Bioengineering, University of Louisville, Louisville, USA.

Medical Physics
|September 23, 2025
PubMed
Summary

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This study introduces a deep learning model using dual mammogram views to improve breast cancer (BC) classification. The novel approach enhances diagnostic accuracy for dense breast tissue, aiding in earlier detection.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Dense breast tissue is a significant risk factor for breast cancer (BC).
  • Current mammographic classification of BC is often subjective and unreliable, hindering accurate evaluation.
  • Improving BC classification accuracy is critical for patient outcomes.

Purpose of the Study:

  • To develop a deep learning method with a local context attention module (LCAM) for enhanced BC classification.
  • To improve grading consistency and accuracy using dual mammogram views aligned with BI-RADS categories.
  • To leverage local context around breast masses for more precise BC evaluation.

Main Methods:

  • Identified regions of interest (ROIs) with dense tissue around breast masses from dual mammogram views.
Keywords:
BI‐RADSbreast cancerdeep learningmammogrammass malignancy

Related Experiment Videos

Last Updated: May 3, 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

3.6K
  • Utilized a convolutional neural network (CNN)-based model incorporating LCAM for feature selection and differentiation.
  • LCAM inferred attention maps along channel and spatial dimensions for adaptive feature refinement.

    • Main Results:

    • The framework was evaluated on 3020 patients across four BI-RADS categories.
    • Achieved a sensitivity of 82.46% and a specificity of 91.42% in identifying BI-RADS grading for breast masses.
    • Demonstrated robust performance in classifying BC using dual mammogram views.

    Conclusions:

    • Introduced a novel CNN-based framework utilizing dual mammogram views for BC classification.
    • The LCAM effectively captures local characteristics surrounding breast masses, enhancing classification accuracy.
    • The proposed method aims to improve the consistency and reliability of BC classification outcomes.