[论文解读] Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

Afshin Shoeibi, Marjane Khodatars+13 more|arXiv (Cornell University)|Jul 16, 2020

COVID-19 diagnosis using AI参考文献 240被引用 148

一句话总结

对深度学习模型如何用于COVID-19从X光/CT检测、分割和预测的全面综述，包括数据来源与挑战。

ABSTRACT

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed.

研究动机与目标

• 总结深度学习网络在使用X-ray和CT图像进行COVID-19检测与分割方面的应用。
• 评估基于DL的COVID-19跨区域传播预测方法。
• 识别在DL COVID-19研究中使用的公共数据集和预处理实践。
• 讨论基于DL的COVID-19诊断与预测中的挑战及未来研究方向。

提出的方法

• 综述COVID-19研究中用于分类、分割和预测的DL架构。
• 讨论数据增强和预训练模型以缓解数据有限问题。
• 概述像U-Net、SegNet、FCN等分割模型及其在肺部轮廓识别中的作用。
• 描述包括RNNs及LSTM/GRU变体在内的预测模型。
• 探索先进的AI趋势（注意力、transformers、数据融合、图DL）。

实验结果

研究问题

• RQ1 哪些DL架构和训练策略对于X-ray和CT图像的COVID-19检测最为有效？
• RQ2 分割与预测的DL方法如何促成COVID-19诊断与传播预测？
• RQ3 在DL COVID-19研究中常用的公共数据集和预处理步骤有哪些？
• RQ4 在将DL应用于COVID-19检测和预测时，哪些是关键挑战与未来方向？

主要发现

• 基于DL的CADS使用X-ray/CT在大量研究中达到较高的准确率（各种报告的指标）。
• 像U-Net和SegNet这样的分割模型在COVID-19影像中的肺部轮廓辨识中被广泛使用。
• 预测DL方法主要使用RNN/LSTM/GRU来对时序数据进行建模以进行传播预测。
• 注意力、transformer、数据融合和图DL方法正成为COVID-19诊断与预后的新兴趋势。
• 存在丰富的公共数据集和数据增强策略以解决COVID-19数据有限的问题，尽管预测数据仍相对稀缺。