The coronavirus disease 2019 (COVID-19) pandemic has imposed dramatic challenges to health-care organizations worldwide. To combat the global crisis, the use of thoracic imaging has played a major role in the diagnosis, prediction, and management of COVID-19 patients with moderate to severe symptoms or with evidence of worsening respiratory status. In response, the medical image analysis community acted quickly to develop and disseminate deep learning models and tools to meet the urgent need of managing and interpreting large amounts of COVID-19 imaging data. This review aims to not only summarize existing deep learning and medical image analysis methods but also offer in-depth discussions and recommendations for future investigations. We believe that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence.

中文翻译：

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用于 COVID-19 诊断和预测的深度学习和医学图像分析

2019 年冠状病毒病 (COVID-19) 大流行给全球医疗保健组织带来了巨大挑战。为了应对全球危机，胸部影像学的使用在诊断、预测和管理具有中度至重度症状或有呼吸状况恶化证据的 COVID-19 患者方面发挥了重要作用。对此，医学图像分析界迅速采取行动，开发和传播深度学习模型和工具，以满足管理和解释大量 COVID-19 成像数据的迫切需求。本综述不仅旨在总结现有的深度学习和医学图像分析方法，还为未来的研究提供深入的讨论和建议。我们相信，高质量、精心策划和基准化的 COVID-19 成像数据集的广泛可用性为在数据科学和人工智能前沿开发、验证和传播新颖的深度学习方法提供了变革性测试平台的巨大希望。