A multiple Kernel learning algorithm for cell nucleus classification of renal cell carcinoma

Authors:
Peter Schüffler;Aydin Ulas;Umberto Castellani;Vittorio Murino
Affiliations:
ETH Zürich, Department of Computer Science, Zürich, Switzerland;University of Verona, Department of Computer Science, Verona, Italy;University of Verona, Department of Computer Science, Verona, Italy;University of Verona, Department of Computer Science, Verona and Istituto Italiano di Tecnologia, Genova, Italy
Venue:
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Year:
2011

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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.