Multiclass Classification of Breast Cancer in Whole-Slide Images

  • Scotty KwokEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)


Breast cancer is one of the leading cause of cancer-related death worldwide. During the diagnosis of breast cancer, the histopathological assessment of Haemotoxylin and Eosin(H&E) stained slides provides important clinical values. By applying computer-aid diagnosis on whole-slide image(WSI), the efficiency and consistency of such assessment could be improved. In this paper, we propose a deep learning-based framework that classifies H&E stained WSIs into regions of normal tissue, benign lesion, in-situ carcinoma and invasive carcinoma. The framework utilizes both microscopy images and WSIs to train a patch classifier in two stages. The underlying classifier is based on Inception-Resnet-v2. This framework won both parts of the ICIAR2018 Grand Challenge on Breast Cancer Histology Images [4] competition, achieved a part A multiclass accuracy of 87% and part B score of 0.6929.


Breast cancer Deep learning Whole-Slide Images Multiclass classification 



We would like to thank the organizers of ICIAR2018 and BACH2018 who supported and organized this challenge.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    ICIAR 2018 Grand Challenge on Breast Cancer Histology Images (2018).
  5. 5.
    Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., Campilho, A.: Classification of breast cancer histology images using convolutional neural networks. PLOS ONE 12(6), 1–14 (2017). Scholar
  6. 6.
    Elmore, J.G., Longton, G.M., Carney, P.A., Geller, B.M., Onega, T., Tosteson, A.N.A., et al.: Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11), 1122–1132 (2015). Scholar
  7. 7.
    Habibzadeh, M.N., Jannesary, M., Aboulkheyr, H., Khosravi, P., Elemento, O., Totonchi, M., Hajirasouliha, I.: Breast cancer histopathological image classification: a deep learning approach. bioRxiv (2018).
  8. 8.
    Jain, R.K., Mehta, R., Dimitrov, R., Larsson, L.G., Musto, P.M., Hodges, K.B., Ulbright, T.M., Hattab, E.M., Agaram, N., Idrees, M.T., Badve, S.: Atypical ductal hyperplasia: interobserver and intraobserver variability. Mod. Pathol. 24, 917–923 (2011)CrossRefGoogle Scholar
  9. 9.
    Janowczyk, A., Madabhushi, A.: Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases. J. Pathol. Inform. 7(1), 29 (2016).;year=2016;volume=7;issue=1;spage=29;epage=29;aulast=Janowczyk;t=6CrossRefGoogle Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Schnitt, S., Connolly, J., Tavassoli, F.A., Fechner, R., Kempson, R.L., Gelman, R., Page, D.: Interobserver reproducibility in the diagnosis of ductal proliferative breast lesions using standardized criteria. Am. J. Surg. Pathol. 16(12), 1133–1143 (1992)CrossRefGoogle Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv e-prints, September 2014Google Scholar
  13. 13.
    Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. IEEE Trans. Biomed. Eng. 63(7), 1455–1462 (2016)CrossRefGoogle Scholar
  14. 14.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, ArXiv e-prints, February 2016Google Scholar
  15. 15.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going Deeper with Convolutions. ArXiv e-prints, September 2014Google Scholar
  16. 16.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Seek AI LimitedHong KongChina

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