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Related Experiment Video

Updated: Dec 23, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Deep learning for mass detection in Full Field Digital Mammograms.

Richa Agarwal1, Oliver Díaz2, Moi Hoon Yap3

  • 1VICOROB, Department of Computer Architecture and Technology, University of Girona, Spain.

Computers in Biology and Medicine
|April 28, 2020
PubMed
Summary

This study introduces an automated deep learning framework using Faster Region-based Convolutional Neural Network (Faster-RCNN) for detecting breast masses in mammograms. The system achieves high accuracy, demonstrating potential for advanced computer-aided diagnosis (CAD) in breast cancer screening.

Keywords:
CNNDeep learningFFDMMammogramMass detection

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Machine Learning for Diagnostics

Background:

  • Convolutional Neural Networks (CNNs) show promise in medical imaging for mass detection and classification.
  • Existing methods for mass detection in Full-Field Digital Mammograms (FFDM) can be improved with advanced AI techniques.

Purpose of the Study:

  • To develop and evaluate a fully automated deep learning framework for detecting masses in FFDMs.
  • To benchmark the performance of deep learning models on the large-scale OPTIMAM Mammography Image Database (OMI-DB).

Main Methods:

  • Utilized the Faster Region-based Convolutional Neural Network (Faster-RCNN) model for mass detection.
  • Applied the framework to the OMI-DB (∼80,000 FFDMs) from Hologic and General Electric (GE) scanners.
  • Employed transfer learning with Faster-RCNN on Hologic data for GE and INbreast (Siemens) datasets.

Main Results:

  • Achieved a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on Hologic scanner FFDMs.
  • Obtained a TPR of 0.91±0.06 at 1.69 FPI for GE scanner images.
  • Demonstrated superior performance on the INbreast dataset compared to state-of-the-art methods (TPR of 0.99±0.03 for malignant, 0.85±0.08 for benign masses).

Conclusions:

  • The proposed automated Faster R-CNN framework effectively detects breast masses in FFDMs across different scanner types.
  • The study highlights the potential of this deep learning approach as a component of advanced computer-aided diagnosis (CAD) systems for breast cancer screening.
  • This research provides the first deep learning performance benchmark on the OMI-DB dataset.