Robust Active Learning For The Diagnosis Of Parasites

dc.creatorSaito
dc.creatorPriscila T. M.; Suzuki
dc.creatorCelso T. N.; Gomes
dc.creatorJancarlo F.; de Rezende
dc.creatorPedro J.; Falcao
dc.creatorAlexandre X.
dc.date2015-NOV
dc.date2016-06-07T13:19:01Z
dc.date2016-06-07T13:19:01Z
dc.date.accessioned2018-03-29T01:39:16Z
dc.date.available2018-03-29T01:39:16Z
dc.identifier
dc.identifierRobust Active Learning For The Diagnosis Of Parasites. Elsevier Sci Ltd, v. 48, p. 3572-3583 NOV-2015.
dc.identifier0031-3203
dc.identifierWOS:000359028900024
dc.identifier10.1016/j.patcog.2015.05.020
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0031320315001995
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/242608
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1306306
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionWe have developed an automated system for the diagnosis of intestinal parasites from optical microscopy images. The objects (species of parasites and impurities) segmented from these images form a large dataset We are interested in the active learning problem of selecting a reasonably small number of objects to be labeled under an expert's supervision for use in training a pattern classifier. However, impurities are very numerous, constitute several clusters in the feature space, and can be quite similar to some species of parasites, leading to a significant challenge for active learning methods. We propose a technique that pre-organizes the data and then properly balances the selection of samples from all classes and uncertain samples for training. Early data organization avoids reprocessing of the large dataset at each learning iteration, enabling the halting of sample selection after a desired number of samples per iteration, yielding interactive response time. We validate our method by comparing it with state-of-the-art approaches, using a previously labeled dataset of almost 6000 objects. Moreover, we report results from experiments on a very realistic scenario, consisting of a dataset with over 140,000 unlabeled objects, under unbalanced classes, the absence of some classes, and the presence of a very large set of impurities. (C) 2015 Elsevier Ltd. All rights reserved.
dc.description48
dc.description11
dc.description
dc.description3572
dc.description3583
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCAPES [01-P-01965/2012]
dc.descriptionFAPESP [2007/52015-0]
dc.descriptionCNPq [311140/2014-9, 141795/2010-7, 552559/2010-5, 303673/2010-9, 477692/2012-5]
dc.description
dc.description
dc.description
dc.languageen
dc.publisherELSEVIER SCI LTD
dc.publisher
dc.publisherOXFORD
dc.relationPATTERN RECOGNITION
dc.rightsembargo
dc.sourceWOS
dc.subjectImage Annotation
dc.subjectText Classification
dc.subjectWeb Images
dc.subjectPath
dc.subjectPropagation
dc.subjectRetrieval
dc.subjectSvms
dc.titleRobust Active Learning For The Diagnosis Of Parasites
dc.typeArtículos de revistas


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