用于胸部X光片疾病风险预测的自监督预训练方法比较
A Comparison of Self-Supervised Pretraining Approaches for Predicting Disease Risk from Chest Radiograph Images
AI总结：
针对医学风险预测标注数据稀缺的问题，该研究对比了多种半监督与自监督预训练方法在胸部X光片死亡风险预测任务上的效果，发现半监督自编码器在内外验证中均优于对比学习和迁移学习。
摘要：
深度学习是医学影像任务的当前最优技术，但需要大型标注数据集。在风险预测领域，大型数据集十分稀缺，因为这类数据需要同时包含影像信息和随访数据（如诊断编码）。不过，带有诊断标签的公开影像数据集的发布，为自监督和半监督方法提升风险预测任务的标签利用效率提供了机遇。尽管已有多项研究在自然图像分类、目标检测和医学影像解读领域对比了自监督方法，但针对哪些方法能学习到适用于风险预测的鲁棒表征，相关研究数据仍很有限。我们对半监督和自监督学习方法用于胸部X光片死亡风险预测的效果开展了对比研究。结果发现，半监督自编码器在内部验证和外部验证中的表现均优于对比学习和迁移学习方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
