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Renal Cancer Cell Classification Using Generative Embeddings and Information Theoretic Kernels

  • Manuele Bicego
  • Aydın Ulaş
  • Peter Schüffler
  • Umberto Castellani
  • Vittorio Murino
  • André Martins
  • Pedro Aguiar
  • Mario Figueiredo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)

Abstract

In this paper, we propose a hybrid generative/discriminative classification scheme and apply it to the detection of renal cell carcinoma (RCC) on tissue microarray (TMA) images. In particular we use probabilistic latent semantic analysis (pLSA) as a generative model to perform generative embedding onto the free energy score space (FESS). Subsequently, we use information theoretic kernels on these embeddings to build a kernel based classifier on the FESS. We compare our results with support vector machines based on standard linear kernels and RBF kernels; and with the nearest neighbor (NN) classifier based on the Mahalanobis distance using a diagonal covariance matrix. We conclude that the proposed hybrid approach achieves higher accuracy, revealing itself as a promising approach for this class of problems.

Keywords

Support Vector Machine Renal Cell Carcinoma Renal Cancer Cell Near Neighbor Latent Dirichlet Allocation 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Manuele Bicego
    • 1
  • Aydın Ulaş
    • 1
  • Peter Schüffler
    • 2
  • Umberto Castellani
    • 1
  • Vittorio Murino
    • 1
    • 3
  • André Martins
    • 4
    • 6
  • Pedro Aguiar
    • 5
    • 6
  • Mario Figueiredo
    • 4
    • 6
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly
  2. 2.Department of Computer ScienceETH ZürichZürichSwitzerland
  3. 3.Istituto Italiano di TecnologiaGenovaItaly
  4. 4.Instituto de TelecomunicaçõesLisboaPortugal
  5. 5.Instituto de Sistemas e RobóticaLisboaPortugal
  6. 6.Instituto Superior TécnicoTechnical University of LisbonPortugal

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