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Evolutionary multi-target neural network architectures for flow void analysis in optical coherence tomography angiography
dc.contributor.author | López-Varela, Emilio | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Fernández-Vigo, José Ignacio | |
dc.contributor.author | Moreno-Morillo, Francisco Javier | |
dc.contributor.author | García-Feijóo, Julián | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-04-15T18:39:40Z | |
dc.date.available | 2024-04-15T18:39:40Z | |
dc.date.issued | 2024-03 | |
dc.identifier.citation | López-Varela, E., de Moura, J., Novo, J., Fernández-Vigo, J. I., Moreno-Morillo, F. J., García-Feijóo, J., & Ortega, M. (2024). Evolutionary multi-target neural network architectures for flow void analysis in optical coherence tomography angiography. Applied Soft Computing, 153, 111304. | es_ES |
dc.identifier.issn | 1568-4946 | |
dc.identifier.issn | 1872-9681 | |
dc.identifier.uri | http://hdl.handle.net/2183/36204 | |
dc.description.abstract | [Abstract]: Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality used to evaluate the retinal microvasculature. Recent advances in OCTA allows to visualize the blood flow within the choriocapillaris region, where a granular image is obtained showing a pattern of small dark regions, called flow voids (FVs). Given its relevance, numerous clinical studies have linked the changes in FVs distribution to multiple diseases. The granular structure of these images makes accurate labeling and segmentation difficult, which can be overcome by using a multi-target perspective. However, manually designing a neural architecture that can accurately predict all targets in a balanced way is a major challenge. In this work, we propose a novel methodology based on evolutionary multi-target optimized networks that, through a set of evolutionary operators, traverses a search space of architectures in a deep but efficient way. This methodology allows us to discover efficient and accurate multi-target architectures tailored to our problem, but which are also adaptable to other tasks due to their robustness. To validate and analyze our methodology and the discovered network model, we performed extensive experimentation with cases from a real clinical study, achieving better results than the state of the art and manually designed architectures. | es_ES |
dc.description.sponsorship | This research was funded by Government of Spain, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018- 095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with reference PID2019-108435RB-I00, reference PDC2022-133132-I00 and TED2021- 131201B-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, as Research Center accredited by Galician University System, is funded by ‘‘Consellería de Cultura, Educación e Universidade from Xunta de Galicia’’, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by ‘‘Secretaría Xeral de Universidades’’, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. Funding for open access charge: Universidade da Coruña/CISUG. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACION Y CARACTERIZACION COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLOGICA: ESTUDIOS EN ESCLEROSIS MULTIPLE | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/TED2021-131201B-I00/ES/DIAGNÓSTICO DIGITAL: TRANSFORMACIÓN DE LA DETECCIÓN DE ENFERMEDADES NEUROVASCULARES Y DEL TRATAMIENTO DE LOS PACIENTES | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/PDC2022-133132-I00/ES/MEJORAS EN EL DIAGNÓSTICO E INVESTIGACIÓN CLÍNICO MEDIANTE TECNOLOGÍAS INTELIGENTES APLICADAS LA IMAGEN OFTALMOLÓGICA | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.asoc.2024.111304 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Central serous chorioretinopathy | es_ES |
dc.subject | Evolutionary neural networks | es_ES |
dc.subject | Flow voids | es_ES |
dc.subject | Multi-target | es_ES |
dc.subject | OCTA imaging | es_ES |
dc.title | Evolutionary multi-target neural network architectures for flow void analysis in optical coherence tomography angiography | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Applied Soft Computing | es_ES |
UDC.volume | 153 | es_ES |
UDC.issue | 111304 | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 17 | es_ES |
dc.identifier.doi | 10.1016/j.asoc.2024.111304 |
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