用百万张无标注胸片训练通用模型，提升疾病诊断泛化能力
A foundation model for generalizable disease diagnosis in chest X-ray images
- 基于104万张无标签胸片进行自监督预训练，学习通用图像特征
- 微调后在多个任务中实现高精度疾病分类，减少对标注数据依赖
- 适合需要跨场景部署的医疗AI研发人员使用
医学人工智能正在革新胸片（CXR）图像的解读，提供强大的疾病诊断工具。然而，这些AI模型的效能常受限于对大量特定任务标注数据的依赖，以及在不同临床环境中的泛化能力不足。为解决这些问题，我们提出CXBase模型，该模型通过自监督学习从104万张无标签胸片中学习通用表征，从而高效适配多种临床任务。模型首先在大规模无标签数据上预训练，无需人工标注即可捕捉有意义的模式。随后通过少量标注数据微调，显著提升疾病检测性能，实现精准分类。该方法为提高模型泛化性、减轻专家标注负担提供了通用解决方案，推动胸片影像中人工智能的广泛应用。
原文摘要 · Abstract (English)
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.
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