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.

翻译：结核病（TB）仍是全球重大卫生挑战，其中儿童病例尤为值得关注。世界卫生组织（WHO）推荐采用胸部X光片（CXR）进行结核病筛查。然而，放射科医师的视觉判读易受主观因素影响，耗时且易出错，尤其在儿童结核病诊断中更为突出。基于人工智能（AI）的计算机辅助检测（CAD）工具，尤其是利用深度学习的系统，在提升肺部疾病检测能力方面展现出潜力。但仍面临数据稀缺和泛化能力不足等挑战。为此，本文提出一种基于Vision Transformers（ViT）的新型自监督学习范式，通过增强CXR中的结核病检测能力，实现零样本儿童结核病诊断。实验表明，与全监督（即无预训练）ViT模型相比，自监督预训练在成人结核病检测中提升约12.7%的AUC和约13.4%的AUPR，在儿童中亦获得同等增益，最终在成人结核病检测中达到0.959 AUC和0.962 AUPR，在零样本儿童结核病检测中达到0.697 AUC和0.607 AUPR。本研究证实，基于成人CXR的自监督学习可有效迁移至数据稀缺的儿童结核病检测等下游任务。