This literature review summarizes the current deep learning methods developed by the medical imaging AI research community that have been focused on resolving lung imaging problems related to coronavirus disease 2019 (COVID-19). COVID-19 shares many of the same imaging characteristics as other common forms of bacterial and viral pneumonia. Differentiating COVID-19 from other common pulmonary infections is a non-trivial task. To help offset what commonly requires hours of tedious manual annotation, several innovative solutions have been published to help healthcare providers during the COVID-19 pandemic. However, the absence of a comprehensive survey on the subject makes it challenging to ascertain which approaches are promising and therefore deserve further investigation. In this survey, we present an in-depth review of deep learning techniques that have recently been applied to the task of discovering the diagnosis and prognosis of COVID-19 patients. We categorize existing approaches based on features such as dimensionality of radiological imaging, system purpose, and used deep learning techniques, underlying core issues, and challenges. We also address the merits and shortcomings of various approaches, and finally we discuss future directions for this research.

这篇文献综述总结了医学影像 AI 研究社区目前开发的深度学习方法，这些方法一直专注于解决与 2019 冠状病毒病 (COVID-19) 相关的肺部影像问题。COVID-19 与其他常见形式的细菌性和病毒性肺炎具有许多相同的成像特征。将 COVID-19 与其他常见肺部感染区分开来是一项非常重要的任务。为了帮助抵消通常需要数小时繁琐的手动注释的工作，已经发布了几种创新解决方案，以在 COVID-19 大流行期间帮助医疗保健提供者。然而，由于缺乏对该主题的全面调查，因此很难确定哪些方法有前途，因此值得进一步研究。在本次调查中，我们对最近应用于发现 COVID-19 患者诊断和预后任务的深度学习技术进行了深入回顾。我们根据放射成像的维度、系统目的和使用的深度学习技术、潜在核心问题和挑战等特征对现有方法进行分类。我们还讨论了各种方法的优点和缺点，最后我们讨论了这项研究的未来方向。