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dc.contributor.authorGende, M.
dc.contributor.authorMoura, Joaquim de
dc.contributor.authorNovo Buján, Jorge
dc.contributor.authorOrtega Hortas, Marcos
dc.date.accessioned2024-05-07T09:41:37Z
dc.date.available2024-05-07T09:41:37Z
dc.date.issued2022-05-15
dc.identifier.citationGende, M., de Moura, J., Novo, J., Ortega, M. (2022). High/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networks. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_18es_ES
dc.identifier.isbn978-3-031-06427-2
dc.identifier.isbn978-3-031-06426-5
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/2183/36424
dc.description21st International Conference, Lecce, Italy, May 23–27, 2022es_ES
dc.description.abstract[Absctract]: Recent advances in artificial intelligence and deep learning models are contributing to the development of advanced computer-aided diagnosis (CAD) systems. In the context of medical imaging, Optical Coherence Tomography (OCT) is a valuable technique that is able to provide cross-sectional visualisations of the ocular tissue. However, OCT is constrained by a limitation between the quality of the visualisations that it can produce and the overall amount of tissue that can be analysed at once. This limitation leads to a scarcity of high quality data, a problem that is very prevalent when developing machine learning-based CAD systems intended for medical imaging. To mitigate this problem, we present a novel methodology for the unpaired conversion of OCT images acquired with a low quality extensive scanning preset into the visual style of those taken with a high quality intensive scan and vice versa. This is achieved by employing contrastive unpaired translation generative adversarial networks to convert between the visual styles of the different acquisition presets. The results we obtained in the validation experiments show that these synthetic generated images can mirror the visual features of the original ones while preserving the natural tissue texture, effectively increasing the total number of available samples that can be used to train robust machine learning-based CAD systems.es_ES
dc.description.sponsorshipThis research was funded by Instituto de Salud Carlos III, Government of Spain, [DTS18/00136]; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, [RTI2018-095894-B-I00]; Ministerio de Ciencia e Innovación, Government of Spain through the research project [PID2019-108435RB-I00]; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, [ED431C 2020/24], predoctoral grant [ED481A 2021/161]; Axencia Galega de Innovación (GAIN), Xunta de Galicia, [IN845D 2020/38]; CITIC, Centro de Investigación de Galicia [ED431G 2019/01], receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2020/24es_ES
dc.description.sponsorshipXunta de Galicia; ED481A 2021/161es_ES
dc.description.sponsorshipXunta de Galicia; IN845D 2020/38es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relationinfo: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 clínicaes_ES
dc.relationinfo: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 OFTALMOLOGICAes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLEes_ES
dc.relation.urihttps://doi.org/10.1007/978-3-031-06427-2_18es_ES
dc.rights© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGes_ES
dc.subjectComputer-aided Diagnosises_ES
dc.subjectOptical Coherence Tomographyes_ES
dc.subjectEpiretinal Membranees_ES
dc.subjectSegmentationes_ES
dc.subjectDeep Learninges_ES
dc.titleHigh/Low Quality Style Transfer for Mutual Conversion of OCT Images Using Contrastive Unpaired Translation Generative Adversarial Networkses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleLecture Notes in Computer Sciencees_ES
UDC.volume13231es_ES
UDC.startPage210es_ES
UDC.endPage220es_ES
UDC.conferenceTitleICIAP 2022es_ES


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