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Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

  • Alexander RakhlinEmail author
  • Alexey Shvets
  • Vladimir Iglovikov
  • Alexandr A. Kalinin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

Abstract

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to disagreement between pathologists. Computer-aided diagnosis systems show potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.

Keywords

Medical imaging Computer-aided diagnosis (CAD) Computer vision Image recognition Deep learning 

Notes

Acknowledgments

The authors thank the Open Data Science community [18] for useful suggestions and other help aiding the development of this work.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alexander Rakhlin
    • 1
    Email author
  • Alexey Shvets
    • 2
  • Vladimir Iglovikov
    • 3
  • Alexandr A. Kalinin
    • 4
  1. 1.Neuromation OUTallinnEstonia
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.Lyft Inc.San FranciscoUSA
  4. 4.University of MichiganAnn ArborUSA

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