The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the field of medical imaging analysis. The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative, and contrastive approaches. Moreover, the article covers 40 of the most recent research papers in the field of self-supervised learning in medical imaging analysis aiming at shedding the light on the recent innovation in the field. Finally, the article concludes with possible future research directions in the field.
翻译:高品质附加说明的医疗成像数据集稀缺是一个主要问题,与医学成像分析领域的机器学习应用相交,妨碍其进步;自监学习是最近的一个培训范例,它使得能够学习强健的表述,而无需人作说明,这可以被视为是缺乏附加说明医学数据的有效解决办法;本文章回顾了自我监督图像数据图像数据学习方法方面的最先进的研究方向,侧重于其在医学成像分析领域的应用;文章涵盖一套计算机视觉领域最新的自监督学习方法,因为它们适用于医学成像分析,并将它们归类为预测性、基因化和对比性方法;此外,文章还涵盖医学成像分析领域的最新自我监督学习论文中的40篇,目的是介绍该领域最近的创新。最后,文章最后总结了该领域可能的未来研究方向。