Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using
  Self-Supervised Learning
Daniel Capellán-Martín Abhijeet Parida Juan J. Gómez-Valverde Ramon Sanchez-Jacob Pooneh Roshanitabrizi Marius G. Linguraru María J. Ledesma-Carbayo Syed M. Anwar
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.