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：在过去的十年中,在人工附加说明的海量数据方面,监督深入的学习在计算机视觉任务上取得了显著进展。但在医学图像分析中深层学习的应用由于缺少高质量的附加说明的医学成像数据而受到限制。一个新出现的解决办法是自我监督学习(SSL ), 其中对比鲜明的SSL是对抗或优于有监督的学习的最成功的方法。本审查调查了最初以自然图像及其对医疗图像的调整为基础的一些最先进的对比性SSL算法,最后讨论了医学领域应用对比性SSL的最新进展、目前的限制和未来方向。