A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification

  • William LotterEmail author
  • Greg Sorensen
  • David Cox
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)


Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here, we describe a multi-scale convolutional neural network (CNN) trained with a curriculum learning strategy that achieves high levels of accuracy in classifying mammograms. Specifically, we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms, and then use the learned features to initialize a scanning-based model that renders a decision on the whole image, trained end-to-end on outcome data. We demonstrate that our approach effectively handles the “needle in a haystack” nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM dataset.


Learning Strategies Curriculum Mammogram Classification Multi-scale CNN Digital Database For Screening Mammography (DDSM) DDSM Dataset 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Harvard UniversityCambridgeUSA
  2. 2.DeepHealth Inc.CambridgeUSA

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