用于COVID-19分析的深度学习技术及其在Omicron检测中的可用性综述
A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
AI总结：
本文综述了用于COVID-19放射影像分析的深度学习技术，构建分类体系并梳理相关挑战与性能指标，为相关研究提供洞见，助力应对新冠变异株的诊断工具研发。
摘要：
2019年12月爆发的冠状病毒（COVID-19）已成为持续威胁全球人类的公共卫生危机，不仅造成数百万人感染，还重创了全球经济。深度学习（DL）技术已被证明可用于及时分析和勾勒放射影像中的感染区域。本文对深度学习技术开展了深入综述，并基于诊断策略和学习方法构建了分类体系。深度学习技术被系统划分为分类、分割以及多阶段方法三类，分别用于COVID-19的图像级和区域级诊断分析。每一类都包含用于在X射线、计算机断层扫描（CT）等放射影像模态中检测COVID-19感染的预训练和定制化卷积神经网络架构。此外，本文还讨论了开发诊断技术时面临的跨平台互操作性、影像模态检查等挑战，同时综述了这些技术所采用的各类方法和性能指标。本综述为深度学习在放射影像分析领域的前沿研究方向提供了洞见，将进一步推动定制化深度学习诊断工具的研发，以有效应对COVID-19新变异株和新出现的挑战。
英文摘要：
The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological images in a timely manner. This paper makes an in-depth survey of DL techniques and draws a taxonomy based on diagnostic strategies and learning approaches. DL techniques are systematically categorized into classification, segmentation, and multi-stage approaches for COVID-19 diagnosis at image and region level analysis. Each category includes pre-trained and custom-made Convolutional Neural Network architectures for detecting COVID-19 infection in radiographic imaging modalities; X-Ray, and Computer Tomography (CT). Furthermore, a discussion is made on challenges in developing diagnostic techniques such as cross-platform interoperability and examining imaging modality. Similarly, a review of the various methodologies and performance measures used in these techniques is also presented. This survey provides an insight into the promising areas of research in DL for analyzing radiographic images, and further accelerates the research in designing customized DL based diagnostic tools for effectively dealing with new variants of COVID-19 and emerging challenges.
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。
