Portable chest X-ray image generation for the improvement of the automatic COVID-19 screening
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Portable chest X-ray image generation for the improvement of the automatic COVID-19 screeningData
2023Cita bibliográfica
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
Resumo
[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.
Palabras chave
Chest X-ray
Contrastive Unpaired Translation
COVID-19
deep learning
Medical Imaging
Contrastive Unpaired Translation
COVID-19
deep learning
Medical Imaging
Descrición
V XoveTIC Conference. XoveTIC 2022, A Coruña, 5 y 6 de octubre de 2022.