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Portable chest X-ray image generation for the improvement of the 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 | 2024-05-22T08:37:42Z | |
dc.date.available | 2024-05-22T08:37:42Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | D. I. Morís, J. de Moura, J. Novo, M. Ortega, "Portable chest X-ray image generation for the improvement of the automatic COVID-19 screening", in: A. Leitao and L. Ramos (eds.), Proceedings of V XoveTIC Conference. XoveTIC 2022, Kalpa Publications in Computing, vol. 14, pp. 104-107, doi: https://doi.org/10.29007/jq7h | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36569 | |
dc.description | V XoveTIC Conference. XoveTIC 2022, A Coruña, 5 y 6 de octubre de 2022. | es_ES |
dc.description.abstract | [Abstract]: COVID-19 is a disease whose gold standard diagnosis tool, RT-PCR, is unable to provide accurate quantification of its severity in a given patient. Currently, this assessment can be performed with the help of chest X-ray imaging visualization that, however, is a manual, tedious and time-consuming task. In this context, Computer-Aided Diagnosis (CAD) systems are very useful to facilitate the work of clinical specialists in these complex diagnostic tasks, especially in view of recent advances in deep learning techniques in the field of medical image analysis. Despite their great potential, deep learning strategies require a large amount of labelled data, which is often scarce in the context of COVID-19 pandemic. To mitigate these problems, in this work we propose the use of a image translation paradigm, the Cycle-Consistent Adversarial Networks (CycleGAN) to generate a novel set of synthetic images with the aim to improve an automatic COVID-19 screening system using portable chest X-ray images. | es_ES |
dc.description.sponsorship | This research was funded by ISCIII, Government of Spain, DTS18/00136 research project; 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 project with reference PID2019-108435RB-I00; CCEU, Xunta de Galicia through the predoctoral grant contract ref. ED481A 2021/196; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from CCEU, 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.language.iso | eng | es_ES |
dc.publisher | EasyChair | 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/10.29007/jq7h | es_ES |
dc.subject | Chest X-ray | es_ES |
dc.subject | Contrastive Unpaired Translation | es_ES |
dc.subject | COVID-19 | es_ES |
dc.subject | deep learning | es_ES |
dc.subject | Medical Imaging | es_ES |
dc.title | Portable chest X-ray image generation for the improvement of the automatic COVID-19 screening | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Kalpa Publications in Computing | es_ES |
UDC.volume | 14 | es_ES |
UDC.startPage | 104 | es_ES |
UDC.endPage | 107 | es_ES |
dc.identifier.doi | 10.29007/jq7h | |
UDC.conferenceTitle | XoveTIC 2022 | es_ES |