Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

  • Dan C. Cireşan
  • Alessandro Giusti
  • Luca M. Gambardella
  • Jürgen Schmidhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)


We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin.


Ground Truth Input Image Convolutional Neural Network Deep Neural Network Mitotic Nucleus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Behnke, S.: Hierarchical Neural Networks for Image Interpretation. LNCS, vol. 2766. Springer, Heidelberg (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images. In: Neural Information Processing Systems, pp. 2852–2860 (2012)Google Scholar
  3. 3.
    Ciresan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: Flexible, High Performance Convolutional Neural Networks for Image Classification. In: International Joint Conference on Artificial Intelligence, pp. 1237–1242 (2011)Google Scholar
  4. 4.
    Ciresan, D.C., Meier, U., Schmidhuber, J.: Multi-column Deep Neural Networks for Image Classification. In: Computer Vision and Pattern Recognition, pp. 3642–3649 (2012)Google Scholar
  5. 5.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning Hierarchical Features for Scene Labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence (in press, 2013)Google Scholar
  6. 6.
    Fukushima, K.: Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)CrossRefzbMATHGoogle Scholar
  7. 7.
    Geman, S., Bienenstock, E., Doursat, R.: Neural networks and the bias/variance dilemma. Neural Computation 4(1), 1–58 (1992)CrossRefGoogle Scholar
  8. 8.
    Huang, C., Hwee, K.: Automated Mitosis Detection Based on Exclusive Independent Component Analysis. In: Proc. ICPR 2012 (2012)Google Scholar
  9. 9.
    Irshad, H.: Automated mitosis detection in histopathology using morphological and multi-channel statistics features. Journal of Pathology Informatics 4(1) (2013)Google Scholar
  10. 10.
    Khan, A., ElDaly, H., Rajpoot, N.: A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. Journal of Pathology Informatics 4(1) (2013)Google Scholar
  11. 11.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  12. 12.
    Malon, C., Cosatto, E.: Classification of mitotic figures with convolutional neural networks and seeded blob features. Journal of Pathology Informatics 4(1) (2013)Google Scholar
  13. 13.
    Pan, J., Kanade, T., Chen, M.: Heterogeneous conditional random field: Realizing joint detection and segmentation of cell regions in microscopic images. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2940–2947. IEEE (2010)Google Scholar
  14. 14.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)CrossRefGoogle Scholar
  15. 15.
    Roux, L., Racoceanu, D., Loménie, N., Kulikova, M., Irshad, H., Klossa, J., Capron, F., Genestie, C., Naour, G.L., Gurcan, M.N.: Mitosis detection in breast cancer histological images An ICPR 2012 contest. Journal of Pathology Informatics 4(1) (2013)Google Scholar
  16. 16.
    Scherer, D., Müller, A., Behnke, S.: Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition. In: International Conference on Artificial Neural Networks (2010)Google Scholar
  17. 17.
    Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition, pp. 958–963 (2003)Google Scholar
  18. 18.
    Sommer, C., Fiaschi, L., Heidelberg, H., Hamprecht, F., Gerlich, D.: Learning-based mitotic cell detection in histopathological images. In: Proc. ICPR 2012 (2012)Google Scholar
  19. 19.
    Tek, F.: Mitosis detection using generic features and an ensemble of cascade adaboosts. Journal of Pathology Informatics 4(1) (2013)Google Scholar
  20. 20.
    Veta, M., van Diestb, P., Pluim, J.: Detecting mitotic figures in breast cancer histopathology images. In: Proc. of SPIE Medical Imaging (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dan C. Cireşan
    • 1
  • Alessandro Giusti
    • 1
  • Luca M. Gambardella
    • 1
  • Jürgen Schmidhuber
    • 1
  1. 1.IDSIA, Dalle Molle Institute for Artificial IntelligenceUSI-SUPSILuganoSwitzerland

Personalised recommendations