A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images

Deep learning is the state-of-the-art for medical imaging tasks, but requires large, labeled datasets. For risk prediction (e.g., predicting risk of future cancer), large datasets are rare since they require both imaging and long-term follow-up. However, the release of publicly available imaging data with diagnostic labels presents an opportunity for self and semi-supervised approaches to use diagnostic labels to improve label efficiency for risk prediction. Though several studies have compared self-supervised approaches in natural image classification, object detection, and medical image interpretation, there is limited data on which approaches learn robust representations for risk prediction. We present a comparison of semi-and self-supervised learning to predict mortality risk using chest x-ray images. We find that a semi-supervised autoencoder outperforms contrastive and transfer learning in internal and external validation data.