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Related Experiment Video

Updated: Nov 6, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Towards improved breast mass detection using dual-view mammogram matching.

Yutong Yan1, Pierre-Henri Conze2, Mathieu Lamard3

  • 1Inserm, LaTIM UMR 1101, 22 rue Camille Desmoulins, Brest 29238, France; Université de Bretagne Occidentale, 3 rue des Archives, Brest 29238, France; IMT Atlantique, Technopôle Brest-Iroise, Brest 29238, France.

Medical Image Analysis
|May 12, 2021
PubMed
Summary

This study introduces a novel deep learning framework for enhanced breast cancer detection using multi-view mammograms. The system integrates craniocaudal (CC) and mediolateral-oblique (MLO) views, improving mass detection accuracy and providing dual-view mass correspondences for better diagnosis.

Keywords:
Breast cancerComputer-aided diagnosisDual-view matchingInformation fusionMass detectionSiamese networks

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Breast cancer screening relies on analyzing multiple mammogram views.
  • Computer-aided diagnosis (CAD) systems can benefit from integrating multi-view mammogram information.

Purpose of the Study:

  • To develop a multi-tasking deep learning framework for automatic breast mass detection using combined CC and MLO mammograms.
  • To enhance CAD systems by jointly learning mass matching and classification from dual-view mammograms.

Main Methods:

  • A unified Siamese network was proposed for patch-level mass/non-mass classification and dual-view mass matching.
  • The model was integrated into a You-Only-Look-Once (YOLO) based detection pipeline.
  • Exhaustive experiments evaluated the contribution of dual-view matching and classification.

Main Results:

  • The dual-view mass matching significantly improved full-pipeline detection performance, achieving a 94.78% Area Under the Curve (AUC) and 0.8791 classification accuracy.
  • Mass classification was found to enhance mass matching performance, demonstrating task complementarity.
  • The method provides accurate dual-view mass correspondences, aiding interpretation.

Conclusions:

  • The proposed multi-tasking framework effectively leverages multi-view mammograms for improved breast mass detection.
  • This approach can serve as a valuable second opinion tool for mammogram interpretation and breast cancer diagnosis.
  • Integrating mass matching and classification enhances overall detection performance in CAD systems.