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

• URL: http://arxiv.org/abs/2402.14741v1
• Date: Thu, 22 Feb 2024 17:55:18 GMT
• Title: Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning
• Authors: 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
• Abstract summary: The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. Visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. We propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 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.

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