TY - GEN
T1 - Renal cancer cell classification using generative embeddings and information theoretic kernels
AU - Bicego, Manuele
AU - Ulaş, Aydin
AU - Schüffler, Peter
AU - Castellani, Umberto
AU - Murino, Vittorio
AU - Martins, André
AU - Aguiar, Pedro
AU - Figueiredo, Mario
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80455144770&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24855-9_7
DO - 10.1007/978-3-642-24855-9_7
M3 - Conference contribution
AN - SCOPUS:80455144770
SN - 9783642248542
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 75
EP - 86
BT - Pattern Recognition in Bioinformatics - 6th IAPR International Conference, PRIB 2011, Proceedings
T2 - 6th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2011
Y2 - 2 November 2011 through 4 November 2011
ER -