  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.
