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

• URL: http://arxiv.org/abs/2306.08955v1
• Date: Thu, 15 Jun 2023 08:48:14 GMT
• Title: A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images
• Authors: Yanru Chen, Michael T Lu, Vineet K Raghu
• Abstract summary: 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.
• Score: 3.5880535198436156
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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