Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas. Early detection is vital for treatment, yet the lack of skilled radiologists underscores the need for artificial intelligence (AI)-driven screening tools. Developing reliable AI models is challenging due to the necessity for large, high-quality datasets, which are costly to obtain. To tackle this, we propose a teacher--student framework which enhances both disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head. Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection, significantly outperforming baselines. The explainability assessments also show the model bases its predictions on relevant anatomical features, demonstrating promise for deployment in clinical screening and triage settings.

翻译：结核病仍然是一个严峻的全球健康问题，在资源有限和偏远地区尤为突出。早期检测对治疗至关重要，然而熟练放射科医师的匮乏凸显了对人工智能驱动筛查工具的需求。由于需要获取成本高昂的大规模高质量数据集，开发可靠的人工智能模型具有挑战性。为此，我们提出一种师生框架，通过集成两个监督头和一个自监督头，增强胸部X光片的疾病与症状检测能力。我们的模型在区分COVID-19、结核病和正常病例方面达到了98.85%的准确率，在多标签症状检测方面获得了90.09%的宏平均F1分数，显著优于基线模型。可解释性评估还表明，模型的预测基于相关的解剖学特征，展现了在临床筛查和分诊场景中部署的应用前景。