Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Recently, computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. To address this, we establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD. Furthermore, we propose a strong baseline, SymFormer, for simultaneous CXR image classification and TB infection area detection. SymFormer incorporates Symmetric Search Attention (SymAttention) to tackle the bilateral symmetry property of CXR images for learning discriminative features. Since CXR images may not strictly adhere to the bilateral symmetry property, we also propose Symmetric Positional Encoding (SPE) to facilitate SymAttention through feature recalibration. To promote future research on CTD, we build a benchmark by introducing evaluation metrics, evaluating baseline models reformed from existing detectors, and running an online challenge. Experiments show that SymFormer achieves state-of-the-art performance on the TBX11K dataset. The data, code, and models will be released.

翻译：肺结核（TB）是全球性的重大健康威胁，每年导致数百万人死亡。尽管早期诊断和治疗能显著提高生存几率，但这在发展中国家仍是一项重大挑战。近年来，基于深度学习的计算机辅助肺结核诊断（CTD）展现出潜力，但进展受限于有限的训练数据。为解决这一问题，我们构建了一个大规模数据集，即肺结核X光（TBX11K）数据集，包含11,200张胸部X光（CXR）图像及对应的肺结核区域边界框标注。该数据集能支持训练高质量CTD检测器。此外，我们提出了一个强基准模型SymFormer，用于同步进行CXR图像分类和肺结核感染区域检测。SymFormer引入了对称搜索注意力（SymAttention），以利用CXR图像的双侧对称性学习判别特征。由于CXR图像可能不完全符合双侧对称性，我们还提出了对称位置编码（SPE），通过特征重校准增强SymAttention的效果。为促进CTD的后续研究，我们构建了一个基准，包括引入评估指标、评估由现有检测器改造的基准模型，并举办在线挑战赛。实验表明，SymFormer在TBX11K数据集上达到了最先进性能。相关数据、代码和模型将公开发布。