Medical artificial intelligence (AI) is revolutionizing the interpretation of chest X-ray (CXR) images by providing robust tools for disease diagnosis. However, the effectiveness of these AI models is often limited by their reliance on large amounts of task-specific labeled data and their inability to generalize across diverse clinical settings. To address these challenges, we introduce CXRBase, a foundational model designed to learn versatile representations from unlabelled CXR images, facilitating efficient adaptation to various clinical tasks. CXRBase is initially trained on a substantial dataset of 1.04 million unlabelled CXR images using self-supervised learning methods. This approach allows the model to discern meaningful patterns without the need for explicit labels. After this initial phase, CXRBase is fine-tuned with labeled data to enhance its performance in disease detection, enabling accurate classification of chest diseases. CXRBase provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from chest imaging.

本研究针对现有医学人工智能模型在不同临床环境中的泛化能力不足问题，提出了CXRBase，一个从未标记的胸部X光影像中学习通用表示的基础模型。通过自监督学习，CXRBase能够识别有意义的模式，并经过调优后在疾病检测中表现出色，显著提升模型性能，同时减轻专家的标注工作负担，推动胸部影像的广泛临床应用。

通用胸部X光影像疾病诊断的基础模型