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

Peter Schüffler, Aydin Ulaş, Umberto Castellani, Vittorio Murino

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

1 Scopus citations

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.

Original languageEnglish
Title of host publicationImage Analysis and Processing, ICIAP 2011 - 16th International Conference, Proceedings
Pages413-422
Number of pages10
EditionPART 1
DOIs
StatePublished - 2011
Externally publishedYes
Event16th International Conference on Image Analysis and Processing, ICIAP 2011 - Ravenna, Italy
Duration: 14 Sep 201116 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6978 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Image Analysis and Processing, ICIAP 2011
Country/TerritoryItaly
CityRavenna
Period14/09/1116/09/11

Keywords

  • MKL
  • SVM
  • renal cell carcinoma

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