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Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images

2019-07-13Documento de conferência Acesso aberto
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dc.contributor.authorLapa, Paulo
dc.contributor.authorRundo, Leonardo
dc.contributor.authorGonçalves, Ivo
dc.contributor.authorCastelli, Mauro
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.date.accessioned2023-05-11T22:03:37Z
dc.date.available2023-05-11T22:03:37Z
dc.date.issued2019-07-13
dc.descriptionLapa, P., Rundo, L., Gonçalves, I., & Castelli, M. (2019). Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: A study with the semantic learning machine. In GECCO 2019 : Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion (pp. 381-382). (GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion). Association for Computing Machinery, Inc. https://doi.org/10.1145/3319619.3322035 --- This work was partially supported by projects UID/MULTI/00308/2019 and by the European Regional Development Fund through the COMPETE 2020 Programme, FCT - Portuguese Foundation for Science and Technology and Regional Operational Program of the Center Region (CENTRO2020) within project MAnAGER (POCI-01-0145-FEDER-028040). This work was also partially supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project DSAIPA/DS/0022/2018 (GADgET).
dc.description.abstractProstate cancer (PCa) is the most common oncological disease in Western men. Even though a significant effort has been carried out by the scientific community, accurate and reliable automated PCa detection methods are still a compelling issue. In this clinical scenario, high-resolution multiparametric Magnetic Resonance Imaging (MRI) is becoming the most used modality, also enabling quantitative studies. Recently, deep learning techniques have achieved outstanding results in prostate MRI analysis tasks, in particular with regard to image classification. This paper studies the feasibility of using the Semantic Learning Machine (SLM) neuroevolution algorithm to replace the fully-connected architecture commonly used in the last layers of Convolutional Neural Networks (CNNs). The experimental phase considered the PROSTATEx dataset composed of multispectral MRI sequences. The achieved results show that, on the same non-contrast-enhanced MRI series, SLM outperforms with statistical significance a state-of-the-art CNN trained with backpropagation. The SLM performance is achieved without pre-training the underlying CNN with backpropagation. Furthermore, on average the SLM training time is approximately 14 times faster than the backpropagation-based approach.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent2
dc.format.extent399028
dc.identifier.doi10.1145/3319619.3322035
dc.identifier.isbn9781450367486
dc.identifier.otherPURE: 14789493
dc.identifier.otherPURE UUID: 57104bbe-115c-4cb3-9863-d56015b3f70e
dc.identifier.otherScopus: 85066949538
dc.identifier.otherWOS: 000538328100190
dc.identifier.otherORCID: /0000-0002-8793-1451/work/72856208
dc.identifier.urihttp://hdl.handle.net/10362/152620
dc.identifier.urlhttps://www.scopus.com/pages/publications/85066949538
dc.identifier.urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000538328100190
dc.language.isoeng
dc.peerreviewedyes
dc.publisherACM - Association for Computing Machinery
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
dc.subjectClassification
dc.subjectConvolutional Neural Networks
dc.subjectMultiparamet-ric Magnetic Resonance Imaging
dc.subjectNeuroevolution
dc.subjectProstate cancer detection
dc.subjectSemantic Learning Machine
dc.subjectArtificial Intelligence
dc.subjectTheoretical Computer Science
dc.subjectSoftware
dc.subjectSDG 3 - Good Health and Well-being
dc.titleEnhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance imagesen
dc.title.subtitleA study with the semantic learning machineen
dc.typeconference object
degois.publication.firstPage381
degois.publication.lastPage382
degois.publication.titleGECCO 2019
degois.publication.title2019 Genetic and Evolutionary Computation Conference, GECCO 2019
dspace.entity.typePublication
oaire.awardNumberDSAIPA/DS/0022/2018
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0022%2F2018/PT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublicationc35c919f-29eb-4019-b809-622c143b6c56
relation.isProjectOfPublication.latestForDiscoveryc35c919f-29eb-4019-b809-622c143b6c56

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