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Related Experiment Video

Updated: Jul 9, 2026

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

Deep Learning-Based Automated Reports for Breast Density Assessment in Mammography Images.

Juliana H do Prado1, Jan Hurtado2,3, Luiz F T Santos1,4

  • 1Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil.

Journal of Imaging Informatics in Medicine
|July 7, 2026
PubMed
Summary

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This study introduces an AI framework for automated breast density assessment using mammography. The system enhances cancer screening by providing detailed, interpretable reports on breast density patterns.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Breast density is crucial in mammography, impacting cancer risk and detection accuracy.
  • High-density tissue can obscure tumors, reducing screening sensitivity.
  • Accurate breast density assessment is vital for effective cancer risk stratification.

Purpose of the Study:

  • To develop a deep learning framework for automated breast density assessment in mammography.
  • To combine BI-RADS classification and dense fibroglandular tissue segmentation for comprehensive analysis.
  • To generate interpretable reports with quantitative metrics and visualizations to aid clinical decision-making.

Main Methods:

  • A deep learning framework integrating BI-RADS classification and dense fibroglandular tissue segmentation was developed.
Keywords:
Breast densityDeep learningImage classificationImage segmentationMammography

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Related Experiment Videos

Last Updated: Jul 9, 2026

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

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

  • A fused dataset from diverse sources with refined annotations was utilized to address data diversity and class imbalance.
  • The framework generates structured reports including spatial, morphological metrics, and enhanced visualizations.

    • Main Results:

    • The proposed framework demonstrated strong performance on internal datasets and reasonable generalization on external datasets.
    • The system successfully combined classification and segmentation tasks with additional quantitative metrics.
    • Structured reports provided interpretable outputs, aiding in the analysis of breast density patterns.

    Conclusions:

    • The developed deep learning system offers a reproducible and interpretable approach for breast density analysis.
    • The framework's structured reports can complement BI-RADS predictions, especially in challenging cases.
    • This approach may form the basis for standardized and explainable reporting in breast imaging.