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 | 2024-05-15T12:34:07Z | |
dc.date.available | 2024-05-15T12:34:07Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | D. I. Morís, J. de Moura, J. Novo and M. Ortega, "Cycle Generative Adversarial Network Approaches to Produce Novel Portable Chest X-Rays Images for Covid-19 Diagnosis," ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021, pp. 1060-1064, doi: 10.1109/ICASSP39728.2021.9414031 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/36478 | |
dc.description | © 2021 IEEE. This version of the paper has been accepted for publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published paper is available online at: https://doi.org/10.1109/ICASSP39728.2021.9414031 | es_ES |
dc.description | Presentado en: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada, 2021 | es_ES |
dc.description.abstract | [Abstract]: Coronavirus Disease 2019 (COVID-19), declared a global pandemic by the World Health Organization, mainly affects the pulmonary tissues, playing chest X-ray images an important role for its screening and early detection. In this context, portable X-ray devices are widely used, representing an alternative to fixed devices in order to reduce risks of cross-contamination. However, they provide lower quality and detailed images in terms of spatial resolution and contrast. In this work, given the low availability of images of this recent disease, we present new approaches to artificially increase the dimensionality of portable chest X-ray datasets for COVID-19 diagnosis. Hence, we combined 3 complementary CycleGAN architectures to perform a simultaneous oversampling using an unsupervised strategy and without the necessity of paired data. Despite the poor quality of the portable X-ray images, we provide an overall accuracy of 92.50% in a COVID-19 screening context, proving their suitability for COVID-19 diagnostic tasks. | es_ES |
dc.description.sponsorship | This research was funded by Instituto de Salud Carlos III, 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; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, 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 Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia. | 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 | Institute of Electrical and Electronics Engineers Inc. | es_ES |
dc.relation.uri | https://doi.org/10.1109/ICASSP39728.2021.9414031 | es_ES |
dc.rights | © 2021 IEEE. | es_ES |
dc.subject | COVID-19 | es_ES |
dc.subject | CycleGAN | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Oversampling | es_ES |
dc.subject | Portable chest X-ray images | es_ES |
dc.title | Cycle generative adversarial network approaches to produce novel portable chest X-rays images for covid-19 diagnosis | es_ES |
dc.type | conference output | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1109/ICASSP39728.2021.9414031 | |
UDC.conferenceTitle | ICASSP 2021 | es_ES |
UDC.coleccion | Investigación | es_ES |
UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | es_ES |
UDC.grupoInv | Grupo de Visión Artificial e Recoñecemento de Patróns (VARPA) | es_ES |
dc.relation.projectID | 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.projectID | 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.projectID | 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 |