Pre-training datasets, like ImageNet, have become the gold standard in medical image analysis. However, the emergence of self-supervised learning (SSL), which leverages unlabeled data to learn robust features, presents an opportunity to bypass the intensive labeling process. In this study, we explored if SSL for pre-training on non-medical images can be applied to chest radiographs and how it compares to supervised pre-training on non-medical images and on medical images. We utilized a vision transformer and initialized its weights based on (i) SSL pre-training on natural images (DINOv2), (ii) SL pre-training on natural images (ImageNet dataset), and (iii) SL pre-training on chest radiographs from the MIMIC-CXR database. We tested our approach on over 800,000 chest radiographs from six large global datasets, diagnosing more than 20 different imaging findings. Our SSL pre-training on curated images not only outperformed ImageNet-based pre-training (P<0.001 for all datasets) but, in certain cases, also exceeded SL on the MIMIC-CXR dataset. Our findings suggest that selecting the right pre-training strategy, especially with SSL, can be pivotal for improving artificial intelligence (AI)'s diagnostic accuracy in medical imaging. By demonstrating the promise of SSL in chest radiograph analysis, we underline a transformative shift towards more efficient and accurate AI models in medical imaging.

翻译：预训练数据集（如ImageNet）已成为医学图像分析领域的黄金标准。然而，自监督学习（SSL）的兴起——通过利用无标注数据学习鲁棒特征——为绕过繁琐的标注过程提供了契机。本研究探究了在非医学图像上采用SSL预训练方法是否可应用于胸部X光片，并比较了其与基于非医学图像和医学图像的监督预训练的差异。我们采用视觉Transformer模型，分别基于以下三种权重初始化策略进行实验：(i) 自然图像的SSL预训练（DINOv2），(ii) 自然图像的监督预训练（ImageNet数据集），(iii) 基于MIMIC-CXR数据库胸部X光片的监督预训练。我们在六个全球大型数据集的80余万张胸部X光片上验证了该方法，诊断了超过20种影像学发现。结果显示，基于精选图像的SSL预训练不仅全面优于ImageNet预训练（所有数据集P<0.001），在某些情况下甚至超越了基于MIMIC-CXR数据集的监督预训练。研究结果表明，选择恰当的预训练策略（尤其是SSL方法）对提升医学影像人工智能（AI）诊断准确性至关重要。通过展示SSL在胸部X光片分析中的潜力，我们揭示了医学影像领域向更高效、更精准AI模型发展的转型趋势。