Hybrid generative-discriminative nucleus classification of renal cell carcinoma

Aydin Ulaş, Peter J. Schüffler, Manuele Bicego, Umberto Castellani, Vittorio Murino

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

1 Scopus citations

Abstract

In this paper, we propose to use advanced classification techniques with shape features for nuclei classification in tissue microarray images of renal cell carcinoma. Our aim is to improve the classification accuracy in distinguishing between healthy and cancerous cells. The approach is inspired by natural language processing: several features are extracted from the automatically segmented nuclei and quantized to visual words, and their co-occurrences are encoded as visual topics. To this end, a generative model, the probabilistic Latent Semantic Analysis (pLSA) is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input for new classifiers, defining a hybrid generative-discriminative classification algorithm. We compare our results with the same classifiers on the feature set to assess the increase of accuracy when we apply pLSA. We demonstrate that the feature space created using pLSA achieves better accuracies than the original feature space.

Original languageEnglish
Title of host publicationSimilarity-Based Pattern Recognition - First International Workshop, SIMBAD 2011, Proceedings
Pages77-89
Number of pages13
DOIs
StatePublished - 2011
Externally publishedYes
Event1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011 - Venice, Italy
Duration: 28 Sep 201130 Sep 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

Conference

Conference1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011
Country/TerritoryItaly
CityVenice
Period28/09/1130/09/11

Keywords

  • SVM
  • probabilistic Latent Semantic Analysis
  • renal cell carcinoma

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