Enhancing Network Initialization for Medical AI Models Using Large-Scale, Unlabeled Natural Images
This work addresses the challenge of enhancing AI diagnostic accuracy in medical imaging, particularly for chest radiographs, by leveraging unlabeled data, though it is incremental as it builds on existing SSL methods applied to a new domain.
The study tackled the problem of improving diagnostic accuracy in medical AI models by comparing self-supervised learning (SSL) pre-training on natural images to supervised pre-training methods for chest radiograph analysis, finding that SSL outperformed ImageNet-based pre-training and sometimes exceeded supervised pre-training on medical images, with statistical significance (P<0.001) across datasets.
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.