Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning

arXiv:2402.14741v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cases challenge cs.cv detection eess.iv error global global health health interpretation major organization screening self-supervised learning supervised learning tuberculosis type visual world world health organization zero-shot