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Portable chest X-ray image generation for the improvement of the automatic COVID-19 screening

4 pagesPublished: February 16, 2023

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.

Keyphrases: chest x ray, contrastive unpaired translation, covid 19, deep learning, medical imaging

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 104-107.

BibTeX entry
@inproceedings{XoveTIC2022:Portable_chest_X_ray,
  author    = {Daniel I. Morís and Joaquim de Moura and Jorge Novo and Marcos Ortega},
  title     = {Portable chest X-ray image generation for the improvement of the automatic COVID-19 screening},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/Twv8},
  doi       = {10.29007/jq7h},
  pages     = {104-107},
  year      = {2023}}
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