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Deep learning is the state-of-the-art for medical imaging tasks, but requireslarge, labeled datasets. For risk prediction, large datasets are rare sincethey require both imaging and follow-up (e.g., diagnosis codes). However, therelease of publicly available imaging data with diagnostic labels presents anopportunity for self and semi-supervised approaches to improve label efficiencyfor risk prediction. Though several studies have compared self-supervisedapproaches in natural image classification, object detection, and medical imageinterpretation, there is limited data on which approaches learn robustrepresentations for risk prediction. We present a comparison of semi- andself-supervised learning to predict mortality risk using chest x-ray images. Wefind that a semi-supervised autoencoder outperforms contrastive and transferlearning in internal and external validation.

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