Robust Bone Marrow Cell Discrimination by Rotation-Invariant Training of Multi-class Echo State Networks

  • Philipp KainzEmail author
  • Harald Burgsteiner
  • Martin Asslaber
  • Helmut Ahammer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 517)


Classification of cell types in context of the architecture in tissue specimen is the basis of diagnostic pathology and decisions for comprehensive investigations rely on a valid interpretation of tissue morphology. Especially visual examination of bone marrow cells takes a considerable amount of time and inter-observer variability can be remarkable. In this work, we propose a novel rotation-invariant learning scheme for multi-class Echo State Networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity.


Computer-assisted pathology Histopathological image analysis Bone marrow cell classification Echo state networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Al-Janabi, S., Huisman, A., Van Diest, P.J.: Digital pathology: current status and future perspectives. Histopathology 61(1), 1–9 (2011)CrossRefGoogle Scholar
  2. 2.
    Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological image analysis: a review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)CrossRefGoogle Scholar
  3. 3.
    Staroszczyk, T., Osowski, S., Markiewicz, T.: Comparative analysis of feature selection methods for blood cell recognition in leukemia. In: Perner, P. (ed.) MLDM 2012. LNCS, vol. 7376, pp. 467–481. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  4. 4.
    Markiewicz, T., Osowski, S., Marianska, B., Moszczynski, L.: Automatic recognition of the blood cells of myelogenous leukemia using SVM. In: IJCNN, pp. 2496–2501 (2005)Google Scholar
  5. 5.
    Theera-Umpon, N., Dhompongsa, S.: Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification. IEEE Trans. Inf. Technol. Biomed. 11(3), 353–359 (2007)CrossRefGoogle Scholar
  6. 6.
    Sabino, D.M.U., Costa, L.F., Rizzatti, E.G., Zago, M.A.: Toward leukocyte recognition using morphometry, texture and color. In: ISBI, pp. 121–124 (2004)Google Scholar
  7. 7.
    Sjöström, P.J., Frydel, B.R., Wahlberg, L.U.: Artificial neural network-aided image analysis system for cell counting. Cytometry 36(1), 18–26 (1999)CrossRefGoogle Scholar
  8. 8.
    Habibzadeh, M., Krzyżak, A., Fevens, T.: White blood cell differential counts using convolutional neural networks for low resolution images. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 263–274. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Ballarò, B., Florena, A.M., Franco, V., Tegolo, D., Tripodo, C., Valenti, C.: An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders. Med. Image Anal. 12(6), 703–712 (2008)CrossRefGoogle Scholar
  10. 10.
    Nilsson, B., Heyden, A.: Segmentation of complex cell clusters in microscopic images: Application to bone marrow samples. Cytometry A 66A(1), 24–31 (2005)CrossRefGoogle Scholar
  11. 11.
    Jaeger, H.: The“echo state”approach to analysing and training recurrent neural networks - with an erratum note. GMD Report 148 (2001)Google Scholar
  12. 12.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Woodward, A., Ikegami, T.: A reservoir computing approach to image classification using coupled echo state and back-propagation neural networks. In: ICIVC, pp. 543–458 (2011)Google Scholar
  14. 14.
    Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comp. Sci. Review 3(3), 127–149 (2009)CrossRefzbMATHGoogle Scholar
  15. 15.
    Lukoševičius, M.: A practical guide to applying echo state networks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade, 2nd edn. LNCS, vol. 7700, pp. 659–686. Springer, Heidelberg (2012) Google Scholar
  16. 16.
    Verstraeten, D., Dambre, J., Dutoit, X., Schrauwen, B.: Memory versus non-linearity in reservoirs. In: IJCNN, pp. 1–8 (2010)Google Scholar
  17. 17.
    Kainz, P., Mayrhofer-Reinhartshuber, M., Burgsteiner, H., Asslaber, M., Ahammer, H.: Echo state networks for granulopoietic cell recognition in histopathological images of human bone marrow. Biomedizinische Technik 59(S1), S492–S495 (2014)Google Scholar
  18. 18.
    Steil, J.J.: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks 20(3), 353–364 (2007)CrossRefzbMATHGoogle Scholar
  19. 19.
    Werbos, P.J.: Backpropagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)CrossRefGoogle Scholar
  20. 20.
    LeCun, Y., Kavukcuoglu, K., Farabet, C.: Convolutional networks and applications in vision. In: ISCAS, pp. 253–256 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Philipp Kainz
    • 1
    • 2
    Email author
  • Harald Burgsteiner
    • 3
  • Martin Asslaber
    • 4
  • Helmut Ahammer
    • 1
  1. 1.Institute of BiophysicsMedical University of GrazGrazAustria
  2. 2.Institute of NeuroinformaticsUniversity of Zurich and ETH ZurichZurichSwitzerland
  3. 3.Institute for eHealthGraz University of Applied SciencesGrazAustria
  4. 4.Institute of PathologyMedical University of GrazGrazAustria

Personalised recommendations