# A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images Source: [A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:25:24.907Z Authors: Yanru Chen, Michael T Lu, Vineet K Raghu Published: 2023-06-15T08:48:14.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()