Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But the application of deep learning in medical image analysis was limited by the scarcity of high-quality annotated medical imaging data. An emerging solution is self-supervised learning (SSL), among which contrastive SSL is the most successful approach to rivalling or outperforming supervised learning. This review investigates several state-of-the-art contrastive SSL algorithms originally on natural images as well as their adaptations for medical images, and concludes by discussing recent advances, current limitations, and future directions in applying contrastive SSL in the medical domain.

翻译：过去十年，基于手动标注大数据的监督深度学习在计算机视觉任务上取得了显著进展。然而，深度学习在医学图像分析中的应用因高质量标注医学影像数据的稀缺而受到限制。一种新兴解决方案是自监督学习（SSL），其中对比自监督学习是最成功的、可与监督学习媲美甚至超越其性能的方法。本综述研究了最初针对自然图像的多种前沿对比自监督学习算法及其在医学图像上的适配应用，并最后讨论了对比自监督学习在医学领域应用的最新进展、当前局限及未来方向。