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Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images
| dc.contributor.author | Vidal, Plácido | |
| dc.contributor.author | Moura, Joaquim de | |
| dc.contributor.author | Novo Buján, Jorge | |
| dc.contributor.author | Ortega Hortas, Marcos | |
| dc.date.accessioned | 2019-09-12T14:05:35Z | |
| dc.date.available | 2019-09-12T14:05:35Z | |
| dc.date.issued | 2019-08-01 | |
| dc.identifier.citation | Vidal, 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/proceedings2019021034 | es_ES |
| dc.identifier.issn | 2504-3900 | |
| dc.identifier.uri | http://hdl.handle.net/2183/23922 | |
| 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.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
| dc.description.sponsorship | Xunta de Galicia; ED431C 2016-047 | es_ES |
| dc.description.sponsorship | Xunta de Galicia;ED481A-2019/196 | es_ES |
| dc.description.sponsorship | This 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.language.iso | eng | es_ES |
| dc.publisher | MDPI AG | es_ES |
| dc.relation | info: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 | info: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.uri | https://doi.org/10.3390/proceedings2019021034 | es_ES |
| dc.rights | Atribución 4.0 Internacional (CC BY 4.0) | es_ES |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Computer-Aided diagnosis | es_ES |
| dc.subject | Retinal imaging | es_ES |
| dc.subject | Optical Coherence Tomography | es_ES |
| dc.subject | Deep learning | es_ES |
| dc.subject | DenseNet | es_ES |
| dc.subject | Intraretinal cystoid region characterization | es_ES |
| dc.title | Intraretinal Fluid Detection by Means of a Densely Connected Convolutional Neural Network Using Optical Coherence Tomography Images | es_ES |
| dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
| dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
| UDC.journalTitle | Proceedings | es_ES |
| UDC.volume | 21 | es_ES |
| UDC.issue | 1 | es_ES |
| UDC.startPage | 34 | es_ES |
| dc.identifier.doi | 10.3390/proceedings2019021034 | |
| UDC.conferenceTitle | 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019. | es_ES |
