Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography

  • Ayelet Akselrod-Ballin
  • Leonid. Karlinsky
  • Alon Hazan
  • Ran Bakalo
  • Ami Ben Horesh
  • Yoel Shoshan
  • Ella Barkan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


Automatic identification of abnormalities is a key problem in medical imaging. While the majority of previous work in mammography has focused on classification of abnormalities rather than detection and localization, here we introduce a novel deep learning method for detection of masses and calcifications. The power of this approach comes from generating an ensemble of individual Faster-RCNN models each trained for a specific set of abnormal clinical categories, together with extending a modified two stage Faster-RCNN scheme to a three stage cascade. The third stage being an additional classifier working directly on the image pixels with the handful of sub-windows generated by the first two stages. The performance of the algorithm is evaluated on the INBreast benchmark and on a large internal multi-center dataset. Quantitative results compete well with state of the art in terms of accuracy. Computationally the methods runs significantly faster than current state-of-the art techniques.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ayelet Akselrod-Ballin
    • 1
  • Leonid. Karlinsky
    • 1
  • Alon Hazan
    • 1
  • Ran Bakalo
    • 1
  • Ami Ben Horesh
    • 1
  • Yoel Shoshan
    • 1
  • Ella Barkan
    • 1
  1. 1.IBM ResearchHaifaIsrael

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