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Generation of Novel Synthetic Portable Chest X-Ray Images for Automatic COVID-19 Screening
dc.contributor.author | Iglesias Morís, Daniel | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-05-23T06:50:18Z | |
dc.date.available | 2024-05-23T06:50:18Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | D. Iglesias Morís, J. de Moura, J. Novo, and M. Ortega, "Generation of Novel Synthetic Portable Chest X-Ray Images for Automatic COVID-19 Screening", in: Rajae El Ouazzani, Mohammed Fattah, Nabil Benamar (eds.) AI Applications for Disease Diagnosis and Treatment, IGI Global, 2022, doi: https://doi.org/10.4018/978-1-6684-2304-2.ch008 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36585 | |
dc.description | in: Rajae El Ouazzani, Mohammed Fattah, Nabil Benamar (eds.) AI Applications for Disease Diagnosis and Treatment, IGI Global, 2022 | es_ES |
dc.description.abstract | [Abstract]: The diagnosis and the study of the evolution of COVID-19 is crucial to tackle the challenge that this disease represents for healthcare services. Chest x-ray imaging allows us to visualize the pulmonary regions, where COVID-19 causes its main affectation. In order to reduce the risk of cross-contamination, a crucial aspect in the pandemic, portable chest x-ray devices are advantageous being easier to decontaminate in comparison with the fixed machinery, despite offering a lower image quality. Furthermore, the recent emergence of COVID-19 implies a data scarcity that must be tackled. In this chapter, the authors present the analysis of a strategy that generates novel synthetic portable chest x-ray images using the CycleGAN, an architecture for image translation that is trained with unpaired data. The novel set of images is then added to the original dataset, improving the performance of the classification model. | es_ES |
dc.description.sponsorship | This research was funded by Instituto de Salud Carlos III, Government of Spain, [grant number DTS18/00136]; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, [grant number RTI2018-095894-B-I00]; Ministerio de Ciencia e Innovación, Government of Spain through the research project with [grant number PID2019-108435RB-I00]; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the predoctoral and postdoctoral grant contracts [grant number ED481A 2021/196] and [grant number ED481B 2021/059], respectively; and Grupos de Referencia Competitiva, [grant number ED431C 2020/24]; Axencia Galega de Innovación (GAIN), Xunta de Galicia, [grant number IN845D 2020/38]; CITIC, Centro de Investigación de Galicia [grant number 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.sponsorship | Xunta de Galicia; ED481A 2021/196 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN845D 2020/38 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481B 2021/059 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IGI Global | 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 | 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/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLE | es_ES |
dc.relation.uri | https://doi.org/10.4018/978-1-6684-2304-2.ch008 | es_ES |
dc.rights | ©2022 IGI Global. | es_ES |
dc.subject | Computer-Aided Diagnosis | es_ES |
dc.subject | Deep Learning | es_ES |
dc.subject | Biomedical Imaging | es_ES |
dc.subject | Image Generation | es_ES |
dc.subject | CycleGAN | es_ES |
dc.subject | Image Translation | es_ES |
dc.subject | Computer Vision | es_ES |
dc.subject | Classification | es_ES |
dc.title | Generation of Novel Synthetic Portable Chest X-Ray Images for Automatic COVID-19 Screening | es_ES |
dc.type | info:eu-repo/semantics/bookPart | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | AI Applications for Disease Diagnosis and Treatment | es_ES |
dc.identifier.doi | 10.4018/978-1-6684-2304-2.ch008 |