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Enhancing Retinal Blood Vessel Segmentation through Self-Supervised Pre-Training
dc.contributor.author | Morano, José | |
dc.contributor.author | Hervella, Álvaro S. | |
dc.contributor.author | Barreira, Noelia | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Rouco, J. | |
dc.date.accessioned | 2020-10-28T16:23:53Z | |
dc.date.available | 2020-10-28T16:23:53Z | |
dc.date.issued | 2020-08-25 | |
dc.identifier.citation | Morano, J.; Hervella, Á.S.; Barreira, N.; Novo, J.; Rouco, J. Enhancing Retinal Blood Vessel Segmentation through Self-Supervised Pre-Training. Proceedings 2020, 54, 44. https://doi.org/10.3390/proceedings2020054044 | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://hdl.handle.net/2183/26575 | |
dc.description.abstract | [Abstract] The segmentation of the retinal vasculature is fundamental in the study of many diseases. However, its manual completion is problematic, which motivates the research on automatic methods. Nowadays, these methods usually employ Fully Convolutional Networks (FCNs), whose success is highly conditioned by the network architecture and the availability of many annotated data, something infrequent in medicine. In this work, we present a novel application of self-supervised multimodal pre-training to enhance the retinal vasculature segmentation. The experiments with diverse FCN architectures demonstrate that, independently of the architecture, this pre-training allows one to overcome annotated data scarcity and leads to significantly better results with less training on the target task. | es_ES |
dc.description.sponsorship | This work is supported by the Instituto de Salud Carlos III, Government of Spain, and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the RTI2018-095894-B-I00 research projects. In addition, this work has received financial support from the Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%), CITIC, Centro de Investigación del Sistema Universitario de Galicia, Ref. ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DTS18%2F00136/ES/PLATAFORMA ONLINE PARA PREVENCION Y DETECCION PRECOZ DE ENFERMEDAD VASCULAR MEDIANTE ANALISIS AUTOMATIZADO DE INFORMACION E IMAGEN CLINICA/ | |
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 | |
dc.relation.uri | https://doi.org/10.3390/proceedings2020054044 | es_ES |
dc.rights | Atribución 4.0 International (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Self-supervised learning | es_ES |
dc.subject | Transfer learning | es_ES |
dc.subject | Multimodal | es_ES |
dc.subject | Retinal vasculature segmentation | es_ES |
dc.title | Enhancing Retinal Blood Vessel Segmentation through Self-Supervised Pre-Training | 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 | 54 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 44 | es_ES |
dc.identifier.doi | 10.3390/proceedings2020054044 | |
UDC.conferenceTitle | 3rd XoveTIC Conference; A Coruña, Spain; 8–9 October 2020 | es_ES |