A Multiple Kernel Learning Algorithm for Cell Nucleus Classification of Renal Cell Carcinoma

  • Peter Schüffler
  • Aydın Ulaş
  • Umberto Castellani
  • Vittorio Murino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)

Abstract

We consider a Multiple Kernel Learning (MKL) framework for nuclei classification in tissue microarray images of renal cell carcinoma. Several features are extracted from the automatically segmented nuclei and MKL is applied for classification. We compare our results with an incremental version of MKL, support vector machines with single kernel (SVM) and voting. We demonstrate that MKL inherently combines information from different input spaces and creates statistically significantly more accurate classifiers than SVMs and voting for renal cell carcinoma detection.

Keywords

MKL renal cell carcinoma SVM 
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References

  1. 1.
    Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, Banff, Alberta, Canada, pp. 41–48 (2004)Google Scholar
  2. 2.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: CIVR 2007: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, pp. 401–408. ACM, New York (2007)Google Scholar
  3. 3.
    Boykov, Y., Veksler, O., Zabih, R.: Efficient approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(12), 1222–1239 (2001)CrossRefGoogle Scholar
  4. 4.
    Fuchs, T.J., Wild, P.J., Moch, H., Buhmann, J.M.: Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 1–8. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Gönen, M., Alpaydın, E.: Localized multiple kernel learning. In: Proceedings of the International Conference on Machine Learning, ICML 2008, pp. 352–359 (2008)Google Scholar
  6. 6.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using matlab, 993475 (2003)Google Scholar
  7. 7.
    Kononen, J., Bubendorf, L., et al.: Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4(7), 844–847 (1998)CrossRefGoogle Scholar
  8. 8.
    Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. Wiley Interscience, Hoboken (2004)CrossRefMATHGoogle Scholar
  9. 9.
    Lanckriet, G.R.G., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.I.: Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research 5, 27–72 (2004)MathSciNetMATHGoogle Scholar
  10. 10.
    Lee, W.-J., Verzakov, S., Duin, R.P.W.: Kernel combination versus classifier combination. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 22–31. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Moguerza, J.M., Muñoz, A., de Diego, I.M.n.: Improving support vector classification via the combination of multiple sources of information. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 592–600. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6(1), 63–81 (2005)CrossRefMATHGoogle Scholar
  13. 13.
    Schüffler, P.J., Fuchs, T.J., Ong, C.S., Roth, V., Buhmann, J.M.: Computational TMA analysis and cell nucleus classification of renal cell carcinoma. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) Pattern Recognition. LNCS, vol. 6376, pp. 202–211. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Ulaş, A., Semerci, M., Yıldız, O.T., Alpaydın, E.: Incremental construction of classifier and discriminant ensembles. Information Sciences 179(9), 1298–1318 (2009)CrossRefGoogle Scholar
  15. 15.
    Vapnik, V.N.: Statistical learning theory. John Wiley and Sons, Chichester (1998)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peter Schüffler
    • 1
  • Aydın Ulaş
    • 2
  • Umberto Castellani
    • 2
  • Vittorio Murino
    • 2
    • 3
  1. 1.Department of Computer ScienceETH ZürichZürichSwitzerland
  2. 2.Department of Computer ScienceUniversity of VeronaVeronaItaly
  3. 3.Istituto Italiano di Tecnologia (IIT)GenovaItaly

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