Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.

Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images...

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Autores principales:	José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J Dinter, Stefan Schönberg, Matthias Rädle
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8f9eb8535df6447e906a1a7b74306a2f
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spelling	oai:doaj.org-article:8f9eb8535df6447e906a1a7b74306a2f2021-11-18T08:24:59ZIncremental learning with SVM for multimodal classification of prostatic adenocarcinoma.1932-620310.1371/journal.pone.0093600https://doaj.org/article/8f9eb8535df6447e906a1a7b74306a2f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24699716/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.José Fernando García MolinaLei ZhengMetin SertdemirDietmar J DinterStefan SchönbergMatthias RädlePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 4, p e93600 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q José Fernando García Molina Lei Zheng Metin Sertdemir Dietmar J Dinter Stefan Schönberg Matthias Rädle Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.
description	Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.
format	article
author	José Fernando García Molina Lei Zheng Metin Sertdemir Dietmar J Dinter Stefan Schönberg Matthias Rädle
author_facet	José Fernando García Molina Lei Zheng Metin Sertdemir Dietmar J Dinter Stefan Schönberg Matthias Rädle
author_sort	José Fernando García Molina
title	Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.
title_short	Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.
title_full	Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.
title_fullStr	Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.
title_full_unstemmed	Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.
title_sort	incremental learning with svm for multimodal classification of prostatic adenocarcinoma.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/8f9eb8535df6447e906a1a7b74306a2f
work_keys_str_mv	AT josefernandogarciamolina incrementallearningwithsvmformultimodalclassificationofprostaticadenocarcinoma AT leizheng incrementallearningwithsvmformultimodalclassificationofprostaticadenocarcinoma AT metinsertdemir incrementallearningwithsvmformultimodalclassificationofprostaticadenocarcinoma AT dietmarjdinter incrementallearningwithsvmformultimodalclassificationofprostaticadenocarcinoma AT stefanschonberg incrementallearningwithsvmformultimodalclassificationofprostaticadenocarcinoma AT matthiasradle incrementallearningwithsvmformultimodalclassificationofprostaticadenocarcinoma
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Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma.

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