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Improving Breast Cancer Detection Using Symmetry Information with Deep Learning

  • Yeman Brhane HagosEmail author
  • Albert Gubern Mérida
  • Jonas Teuwen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Convolutional Neural Networks (CNN) have had a huge success in many areas of computer vision and medical image analysis. However, there is still an immense potential for performance improvement in mammogram breast cancer detection Computer-Aided Detection (CAD) systems by integrating all the information that radiologist utilizes, such as symmetry and temporal data. In this work, we proposed a patch based multi-input CNN that learns symmetrical difference to detect breast masses. The network was trained on a large-scale dataset of 28294 mammogram images. The performance was compared to a baseline architecture without symmetry context using Area Under the ROC Curve (AUC) and Competition Performance Metric (CPM). At candidate level, AUC value of 0.933 with \(95\%\) confidence interval of \([0.920 ,\ 0.954]\) was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0.929 with \([0.919 ,\ 0.947]\) confidence interval. By incorporating symmetrical information, although there was no a significant candidate level performance again (\(p = 0.111\)), we have found a compelling result at exam level with CPM value of 0.733 (\(p = 0.001\)). We believe that including temporal data, and adding benign class to the dataset could improve the detection performance.

Keywords

Breast cancer Digital mammography Convolutional neural networks Symmetry Deep learning Mass detection 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yeman Brhane Hagos
    • 1
    • 3
    • 4
    • 5
    Email author
  • Albert Gubern Mérida
    • 1
  • Jonas Teuwen
    • 1
    • 2
  1. 1.Department of Radiology and Nuclear MedicineRadboud University Medical CenterNijmegenThe Netherlands
  2. 2.Delft University of TechnologyDelftThe Netherlands
  3. 3.University of BurgundyDijonFrance
  4. 4.University of Cassino and Southern LazioCassinoItaly
  5. 5.University of GironaGironaSpain

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