Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
<|reference_start|>Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning: Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce.<|reference_end|>
@article{capellán-martín2024zero-shot,
    title={Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using
  Self-Supervised Learning},
    author={Daniel Capell'an-Mart'in, Abhijeet Parida, Juan J. G'omez-Valverde,
  Ramon Sanchez-Jacob, Pooneh Roshanitabrizi, Marius G. Linguraru, Mar'ia J.
  Ledesma-Carbayo, Syed M. Anwar},
    journal={21st IEEE International Symposium on Biomedical Imaging (ISBI
  2024), Athens, Greece},
    year={2024},
    doi={10.1109/ISBI56570.2024.10635520},
    archivePrefix={arXiv},
    eprint={2402.14741},
    primaryClass={cs.CV eess.IV}
}