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<p>This research introduces a unique dataset targeting Silicosis, a significant global occupational lung disease, and a member of the Pneumoconiosis family. Addressing the challenges in healthcare data collection and the need for expert annotation, this dataset aims to aid AI algorithms in medical applications. The comprehensive dataset includes not only Silicosis cases but also related conditions, such as tuberculosis and silicotuberculosis, alongside healthy lung images, addressing the diagnostic complexity due to symptom overlap. As the first public dataset of its kind, it offers detailed annotations for lung and disease region segmentation, as well as disease prediction, provided by multiple radiologists. Baseline experiments and findings demonstrate that current AI models have limited predictive accuracy for these disease classes, emphasizing the critical need for dedicated research. It is our assertion that the proposed Silicodata can be a key dataset in designing automated Silicosis detection tools and addressing challenges associated with small sample sizes in medical AI research.</p>

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Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

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  • Yasmeena Akhter,
  • Rishabh Ranjan,
  • Mayank Vatsa,
  • Richa Singh,
  • Santanu Chaudhury,
  • Anjali Agrawal,
  • Shruti Aggarwal,
  • Arjun Kalyanpur,
  • Anurita Menon

摘要

This research introduces a unique dataset targeting Silicosis, a significant global occupational lung disease, and a member of the Pneumoconiosis family. Addressing the challenges in healthcare data collection and the need for expert annotation, this dataset aims to aid AI algorithms in medical applications. The comprehensive dataset includes not only Silicosis cases but also related conditions, such as tuberculosis and silicotuberculosis, alongside healthy lung images, addressing the diagnostic complexity due to symptom overlap. As the first public dataset of its kind, it offers detailed annotations for lung and disease region segmentation, as well as disease prediction, provided by multiple radiologists. Baseline experiments and findings demonstrate that current AI models have limited predictive accuracy for these disease classes, emphasizing the critical need for dedicated research. It is our assertion that the proposed Silicodata can be a key dataset in designing automated Silicosis detection tools and addressing challenges associated with small sample sizes in medical AI research.