A Comparison of Self-Supervised Pretraining Approaches for Predicting
  Disease Risk from Chest Radiograph Images
Yanru Chen Michael T Lu Vineet K Raghu
Deep learning is the state-of-the-art for medical imaging tasks, but requires
large, labeled datasets. For risk prediction, large datasets are rare since
they require both imaging and follow-up (e.g., diagnosis codes). However, the
release of publicly available imaging data with diagnostic labels presents an
opportunity for self and semi-supervised approaches 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.