Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma

Mehmet Gönen, Aydin Ulaş, Peter Schüffler, Umberto Castellani, Vittorio Murino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In kernel-based machine learning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed kernel function. This approach is called multiple kernel learning (MKL). In this paper, we formulate a nonlinear MKL variant and apply it for nuclei classification in tissue microarray images of renal cell carcinoma (RCC). The proposed variant is tested on several feature representations extracted from the automatically segmented nuclei. We compare our results with single-kernel support vector machines trained on each feature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly.

Original languageEnglish (US)
Title of host publicationSimilarity-Based Pattern Recognition - First International Workshop, SIMBAD 2011, Proceedings
Pages250-260
Number of pages11
DOIs
StatePublished - Oct 5 2011
Event1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011 - Venice, Italy
Duration: Sep 28 2011Sep 30 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7005 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011
CountryItaly
CityVenice
Period9/28/119/30/11

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Keywords

  • multiple kernel learning
  • renal cell carcinoma
  • support vector machines

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gönen, M., Ulaş, A., Schüffler, P., Castellani, U., & Murino, V. (2011). Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma. In Similarity-Based Pattern Recognition - First International Workshop, SIMBAD 2011, Proceedings (pp. 250-260). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7005 LNCS). https://doi.org/10.1007/978-3-642-24471-1_18