Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images

  • Navid Alemi KoohbananiEmail author
  • Talha Qaisar
  • Muhammad Shaban
  • Jevgenij Gamper
  • Nasir Rajpoot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)


Breast cancer (BC) is the second most leading cause of cancer deaths in women and BC metastasis accounts for the majority of deaths. Early detection of breast cancer metastasis in sentinel lymph nodes is of high importance for prediction and management of breast cancer progression. In this paper, we propose a novel deep learning framework for automatic detection of micro- and macro- metastasis in multi-gigapixel whole-slide images (WSIs) of sentinel lymph nodes. One of our main contributions is to incorporate a Bayesian solution for the optimization of network’s hyperparameters on one of the largest histology dataset, which leads to 5% gain in overall patch-based accuracy. Furthermore, we present an ensemble of two multi-resolution deep learning networks, one captures the cell level information and the other incorporates the contextual information to make the final prediction. Finally, we propose a two-step thresholding method to post-process the output of ensemble network. We evaluate our proposed method on the CAMELYON16 dataset, where we outperformed “human experts” and achieved the second best performance compared to 32 other competing methods.


  1. 1.
    American Cancer Society: Cancer Facts & Figures (2015)Google Scholar
  2. 2.
    Czerniecki, B.J., et al.: Immunohistochemistry with pancytokeratins improves the sensitivity of sentinel lymph node biopsy in patients with breast carcinoma. Cancer 85(5), 1098–1103 (1999)CrossRefGoogle Scholar
  3. 3.
    Weaver, D.L., et al.: Comparison of pathologist-detected and automated computer-assisted image analysis detected sentinel lymph node micrometastases in breast cancer. Mod. Pathol. 16(11), 1159 (2003)CrossRefGoogle Scholar
  4. 4.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013 Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). Scholar
  5. 5.
    Wang, D., et al.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
  6. 6.
    Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 (2017)CrossRefGoogle Scholar
  7. 7.
    Snoek, J., et al.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951–2959 (2012)Google Scholar
  8. 8.
    Bergstra, J.S., et al.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, pp. 2546–2554 (2011)Google Scholar
  9. 9.
    Szegedy, C., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  10. 10.
    He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  11. 11.
    Huang, G., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1 (2017)Google Scholar
  12. 12.
    Zoph, B., et al.: Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012 (2017)

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Navid Alemi Koohbanani
    • 1
    • 2
    Email author
  • Talha Qaisar
    • 1
  • Muhammad Shaban
    • 1
  • Jevgenij Gamper
    • 1
  • Nasir Rajpoot
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversity of WarwickCoventryUK
  2. 2.The Alan Turing InstituteLondonUK

Personalised recommendations