PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma
Mehmet Gönen, Aydın Ulaş, Peter Schüffler, Umberto Castellani and Vittorio Murino
In: 1st International Workshop on Similarity-Based Pattern Analysis and Recognition, 28-30 Sep 2011, Venice, Italy.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9189
Deposited By:Mehmet Gönen
Deposited On:21 February 2012