自监督预训练对放射影像诊断任务影响的综述
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images
AI总结：
本综述总结自监督预训练在放射影像诊断任务中的效果，发现其通常优于全监督学习，尤其在未标记数据多时，并提出了未来研究方向。
摘要：
自监督预训练已被观察到能有效改善迁移学习的特征表示，利用大量未标记数据。本综述总结了其在X射线、计算机断层扫描、磁共振和超声成像中的近期研究，重点关注比较自监督预训练与全监督学习在分类和分割等诊断任务上的研究。最相关的发现是，与全监督相比，自监督预训练通常能提升下游任务性能，尤其在未标记样本数量远超标记样本时最为显著。基于汇总证据，为考虑使用自监督学习的从业者提供了建议。受当前研究局限性的启发，提出了未来研究的方向和实践，例如将临床知识与具有理论依据的自监督学习方法相结合，在公共数据集上评估，扩大超声领域有限的证据，以及刻画自监督预训练对泛化能力的影响。
英文摘要：
Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.
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。
