Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images

  • Sachin Mehta
  • Ezgi Mercan
  • Jamen Bartlett
  • Donald Weaver
  • Joann G. Elmore
  • Linda Shapiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis. Our method efficiently segments different types of tissues in breast biopsy images while simultaneously predicting a discriminative map for identifying important areas in an image. Our network, Y-Net, extends and generalizes U-Net by adding a parallel branch for discriminative map generation and by supporting convolutional block modularity, which allows the user to adjust network efficiency without altering the network topology. Y-Net delivers state-of-the-art segmentation accuracy while learning \(6.6\times \) fewer parameters than its closest competitors. The addition of descriptive power from Y-Net’s discriminative segmentation masks improve diagnostic classification accuracy by 7% over state-of-the-art methods for diagnostic classification. Source code is available at:



Research reported in this publication was supported by the National Cancer Institute awards R01 CA172343, R01 CA140560, and RO1 CA200690. We would also like to thank NVIDIA Corporation for donating the Titan X Pascal GPU used for this research.


  1. 1.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. TPAMI 39(12), 2481–2495 (2017)CrossRefGoogle Scholar
  2. 2.
    Elmore, J.G., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015)CrossRefGoogle Scholar
  3. 3.
    Fakhry, A., Zeng, T., Ji, S.: Residual deconvolutional networks for brain electron microscopy image segmentation. IEEE Trans. Med. Imaging 36(2), 447–456 (2017)CrossRefGoogle Scholar
  4. 4.
    Fine, R.E.: Diagnostic Techniques, 2nd edn. B. C. Decker Inc, Ontario (2006)Google Scholar
  5. 5.
    Geçer, B.: Detection and classification of breast cancer in whole slide histopathology images using deep convolutional networks. Ph.D. thesis, Bilkent University (2016)Google Scholar
  6. 6.
    He, K., et al.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  7. 7.
    He, K., et al.: Mask R-CNN. In: ICCV (2017)Google Scholar
  8. 8.
    Hou, L., et al.: Patch-based convolutional neural network for whole slide tissue image classification. In: CVPR (2016)Google Scholar
  9. 9.
    Mehta, S., et al.: ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation. arXiv preprint arXiv:1803.06815 (2018)CrossRefGoogle Scholar
  10. 10.
    Mehta, S., et al.: Learning to segment breast biopsy whole slide images. In: WACV (2018)Google Scholar
  11. 11.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MICCAI (2015)Google Scholar
  12. 12.
    Zhao, H., et al.: Pyramid scene parsing network. In: CVPR (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sachin Mehta
    • 1
  • Ezgi Mercan
    • 1
  • Jamen Bartlett
    • 2
  • Donald Weaver
    • 2
  • Joann G. Elmore
    • 3
  • Linda Shapiro
    • 1
  1. 1.University of WashingtonSeattleUSA
  2. 2.University of VermontBurlingtonUSA
  3. 3.University of CaliforniaLos AngelesUSA

Personalised recommendations