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<p>Tuberculosis (TB) is the deadliest disease from a single infectious agent, ranking above malaria, HIV/AIDS and COVID-19. The World Health Organization (WHO) states that treating both active tuberculosis (ATB) and tuberculosis infection (TBI) can reduce tuberculosis mortality to fewer than one death per million by 2050. In this context, the WHO recommends the use of computer-aided detection (CAD) for screening TB as part of a world-wide elimination plan. Recently, CADs have been composed of deep learning models trained with medical images as a tool for classification, segmentation and synthetic image generation. Medical images are scarcer than nature pictures, hence one of the primary gaps in producing more accurate models. Therefore, we propose a framework with three generative adversarial networks (GAN) (i.e., Wasserstein GAN, GAN Pix2Pix, Cycle-GAN) as a synthetic data generate strategy to enlarge TB-related data availability while introducing diversity into the classifier training process of a CAD classifier model. We emphasize that among the synthetic production of chest radiographs (CXR) related to TB, we have created synthetic images from data collected in TBI studies, a novelty to our knowledge.</p>

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Synthetic chest X-ray data generation for tuberculosis infection detection using generative adversarial networks

  • 期刊论文

  • 发表日期：

    2025年6月12日

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  • Otto Tavares Nascimento,
  • José Manoel de Seixas,
  • Anete Trajman

摘要

Tuberculosis (TB) is the deadliest disease from a single infectious agent, ranking above malaria, HIV/AIDS and COVID-19. The World Health Organization (WHO) states that treating both active tuberculosis (ATB) and tuberculosis infection (TBI) can reduce tuberculosis mortality to fewer than one death per million by 2050. In this context, the WHO recommends the use of computer-aided detection (CAD) for screening TB as part of a world-wide elimination plan. Recently, CADs have been composed of deep learning models trained with medical images as a tool for classification, segmentation and synthetic image generation. Medical images are scarcer than nature pictures, hence one of the primary gaps in producing more accurate models. Therefore, we propose a framework with three generative adversarial networks (GAN) (i.e., Wasserstein GAN, GAN Pix2Pix, Cycle-GAN) as a synthetic data generate strategy to enlarge TB-related data availability while introducing diversity into the classifier training process of a CAD classifier model. We emphasize that among the synthetic production of chest radiographs (CXR) related to TB, we have created synthetic images from data collected in TBI studies, a novelty to our knowledge.