Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitle2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.es_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.grupoInvGrupo de Visión Artificial e Recoñecemento de Patróns (VARPA)es_ES
UDC.issue1es_ES
UDC.journalTitleProceedingses_ES
UDC.startPage34es_ES
UDC.volume21es_ES
dc.contributor.authorVidal, Plácido
dc.contributor.authorMoura, Joaquim de
dc.contributor.authorNovo Buján, Jorge
dc.contributor.authorOrtega Hortas, Marcos
dc.date.accessioned2019-09-12T14:05:35Z
dc.date.available2019-09-12T14:05:35Z
dc.date.issued2019-08-01
dc.description.abstract[Abstract] Hereby we present a methodology with the objective of detecting retinal fluid accumulations in between the retinal layers. The methodology uses a robust Densely Connected Neural Network to classify thousands of subsamples, extracted from a given Optical Coherence Tomography image. Posteriorly, using the detected regions, it satisfactorily generates a coherent and intuitive confidence map by means of a voting strategy.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2016-047es_ES
dc.description.sponsorshipXunta de Galicia;ED481A-2019/196es_ES
dc.description.sponsorshipThis research was funded by Instituto de Salud Carlos III grant number DTS18/00136, Ministerio de Economía y Competitividad grant number DPI 2015-69948-R, Xunta de Galicia through the accreditation of Centro Singular de Investigación 2016–2019, Ref. ED431G/01, Xunta de Galicia through Grupos de Referencia Competitiva, Ref. ED431C 2016-047 and Xunta de Galicia predoctoral grant contract ref. ED481A-2019/196
dc.identifier.citationVidal, P.L.; Moura, J.d.; Novo, J.; Ortega, M. Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images. Proceedings 2019, 21, 34. https://doi.org/10.3390/proceedings2019021034es_ES
dc.identifier.doi10.3390/proceedings2019021034
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/23922
dc.language.isoenges_ES
dc.publisherMDPI AGes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2015-69948-R/ES/IDENTIFICACION Y CARACTERIZACION DEL EDEMA MACULAR DIABETICO MEDIANTE ANALISIS AUTOMATICO DE TOMOGRAFIAS DE COHERENCIA OPTICA Y TECNICAS DE APRENDIZAJE MAQUINA
dc.relation.urihttps://doi.org/10.3390/proceedings2019021034es_ES
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectComputer-aided diagnosises_ES
dc.subjectRetinal imaginges_ES
dc.subjectOptical Coherence Tomographyes_ES
dc.subjectDeep learninges_ES
dc.subjectDenseNetes_ES
dc.subjectIntraretinal cystoid region characterizationes_ES
dc.titleIntraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Imageses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication028dac6b-dd82-408f-bc69-0a52e2340a54
relation.isAuthorOfPublication0fcd917d-245f-4650-8352-eb072b394df0
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relation.isAuthorOfPublication.latestForDiscovery028dac6b-dd82-408f-bc69-0a52e2340a54

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