A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

Abstract Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using...

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Autores principales:	Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne L. Martel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/792b44f9bc9942699642a822cd3b0d25
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spelling	oai:doaj.org-article:792b44f9bc9942699642a822cd3b0d252021-12-02T16:07:51ZA Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification10.1038/s41598-018-24876-02045-2322https://doaj.org/article/792b44f9bc9942699642a822cd3b0d252018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-24876-0https://doaj.org/toc/2045-2322Abstract Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.Mohammad PeikariSherine SalamaSharon Nofech-MozesAnne L. MartelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohammad Peikari Sherine Salama Sharon Nofech-Mozes Anne L. Martel A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
description	Abstract Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.
format	article
author	Mohammad Peikari Sherine Salama Sharon Nofech-Mozes Anne L. Martel
author_facet	Mohammad Peikari Sherine Salama Sharon Nofech-Mozes Anne L. Martel
author_sort	Mohammad Peikari
title	A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
title_short	A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
title_full	A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
title_fullStr	A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
title_full_unstemmed	A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification
title_sort	cluster-then-label semi-supervised learning approach for pathology image classification
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/792b44f9bc9942699642a822cd3b0d25
work_keys_str_mv	AT mohammadpeikari aclusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT sherinesalama aclusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT sharonnofechmozes aclusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT annelmartel aclusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT mohammadpeikari clusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT sherinesalama clusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT sharonnofechmozes clusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification AT annelmartel clusterthenlabelsemisupervisedlearningapproachforpathologyimageclassification
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A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

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