医学图像的预测性与对比式自监督学习综述
A Review of Predictive and Contrastive Self-supervised Learning for Medical Images
AI总结：
本文综述了用于医学图像的对比式自监督学习方法，重点分析自然图像SSL算法的医学适配、最新进展与未来方向。
摘要：
在过去十年中，基于人工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。但深度学习在医学图像分析中的应用受到高质量标注医学影像数据稀缺的限制。一个新兴的解决方案是自监督学习（SSL），其中对比式SSL是最成功的方法，能够媲美甚至超越监督学习。本综述考察了若干最初用于自然图像的最先进的对比式SSL算法及其在医学图像上的适应性改进，并在最后讨论了在医学领域应用对比式SSL的最新进展、当前局限性和未来方向。
英文摘要：
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.
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。
