Abstract
Tuberculosis (TB) remains one of the leading causes of mortality worldwide, particularly in resource-limited countries. Chest X-ray (CXR) imaging serves as an accessible and cost-effective diagnostic tool but requires expert interpretation, which is often unavailable. Although machine learning models have shown high performance in TB classification, they often depend on spurious correlations and fail to generalize. Besides, building large datasets featuring high-quality annotations for medical images demands substantial resources and input from domain specialists, and typically involves several annotators reaching agreement, which results in enormous financial and logistical expenses. This study repurposes knowledge distillation technique to train CNN models reducing spurious correlations and localize TB-related abnormalities without requiring bounding-box annotations. By leveraging a teacher-student framework with ResNet50 architecture, the proposed method trained on TBX11k dataset achieve impressive 0.2428 mIOU score. Experimental results further reveal that the student model consistently outperforms the teacher, underscoring improved robustness and potential for broader clinical deployment in diverse settings.
Abstract (translated)
结核病（TB）仍然是全球死亡的主要原因之一，特别是在资源有限的国家。胸部X光片（CXR）成像是一个可获取且成本效益高的诊断工具，但需要专家解读，而这在许多地方并不容易获得。尽管机器学习模型在结核病分类方面表现出色，但它们往往依赖于虚假的相关性，并且难以泛化到不同的场景中去。此外，构建包含高质量标注的大型医学图像数据集需要大量的资源和领域专家的参与，并通常涉及多个注释者达成一致意见的过程，这带来了巨大的财务和后勤成本。 本研究重新利用了知识蒸馏技术来训练卷积神经网络（CNN）模型，以减少虚假相关性并定位结核病相关的异常情况，而无需边界框标注。通过使用基于ResNet50架构的教师-学生框架，在TBX11k数据集上训练的模型取得了令人印象深刻的mIOU评分为0.2428（假设这是一个合理的性能指标表述）。实验结果进一步显示，学生模型在多种情况下均优于教师模型，这表明了其改进后的鲁棒性以及在不同临床环境中更广泛部署的应用潜力。
URL
https://arxiv.org/abs/2512.11057
