Chest X-ray (CXR) is the most frequently ordered imaging test, supporting diverse clinical tasks from thoracic disease detection to postoperative monitoring. However, task-specific classification models are limited in scope, require costly labeled data, and lack generalizability to out-of-distribution datasets. To address these challenges, we introduce CheXFound, a self-supervised vision foundation model that learns robust CXR representations and generalizes effectively across a wide range of downstream tasks. We pretrain CheXFound on a curated CXR-1M dataset, comprising over one million unique CXRs from publicly available sources. We propose a Global and Local Representations Integration (GLoRI) module for downstream adaptations, by incorporating disease-specific local features with global image features for enhanced performance in multilabel classification. Our experimental results show that CheXFound outperforms state-of-the-art models in classifying 40 disease findings across different prevalence levels on the CXR-LT 24 dataset and exhibits superior label efficiency on downstream tasks with limited training data. Additionally, CheXFound achieved significant improvements on new tasks with out-of-distribution datasets, including opportunistic cardiovascular disease risk estimation and mortality prediction. These results highlight CheXFound's strong generalization capabilities, enabling diverse adaptations with improved label efficiency. The project source code is publicly available at https://github.com/RPIDIAL/CheXFound.

翻译：胸部X射线（CXR）是最常开展的影像学检查，可支持从胸部疾病检测到术后监测的多种临床任务。然而，针对特定任务的分类模型存在适用范围有限、需要昂贵标注数据且对分布外数据集泛化能力不足等问题。为应对这些挑战，我们提出了CheXFound——一种通过自监督学习构建的视觉基础模型，能够学习鲁棒的CXR表征，并在广泛的下游任务中实现有效泛化。我们在精心构建的CXR-1M数据集上对CheXFound进行预训练，该数据集包含来自公开资源的超百万张独立CXR影像。针对下游任务适配，我们提出了全局与局部表征融合（GLoRI）模块，通过将疾病特异性局部特征与全局图像特征相结合，提升多标签分类性能。实验结果表明：在CXR-LT 24数据集上，CheXFound对40种不同患病率水平的疾病征象分类性能优于现有最优模型；在训练数据有限的下游任务中展现出卓越的标签效率；在分布外数据集的新任务（包括机会性心血管疾病风险评估和死亡率预测）上取得显著改进。这些结果凸显了CheXFound强大的泛化能力，使其能够以更高的标签效率适应多样化任务。项目源代码已公开于https://github.com/RPIDIAL/CheXFound。