Renal cancer cell classification using generative embeddings and information theoretic kernels

Manuele Bicego, Aydin Ulaş, Peter Schüffler, Umberto Castellani, Vittorio Murino, André Martins, Pedro Aguiar, Mario Figueiredo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics - 6th IAPR International Conference, PRIB 2011, Proceedings
Pages75-86
Number of pages12
DOIs
StatePublished - 2011
Externally publishedYes
Event6th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2011 - Delft, Netherlands
Duration: 2 Nov 20114 Nov 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7036 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2011
Country/TerritoryNetherlands
CityDelft
Period2/11/114/11/11

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