[论文解读] Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images

Shaoping Hu, Yuan Gao+10 more|arXiv (Cornell University)|Apr 14, 2020

COVID-19 diagnosis using AI参考文献 32被引用 28

一句话总结

本文提出了一种弱监督深度学习框架，仅使用图像级别标签即可检测和分类胸部CT扫描中的COVID-19感染，显著减少了耗时的像素级标注需求。该方法在区分COVID-19与非COVID-19病例方面表现出高准确率，显示出在疫情高峰期大规模临床部署的巨大潜力。

ABSTRACT

An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

研究动机与目标

• 解决COVID-19诊断中CT扫描手动标注有限且耗时的问题。
• 开发一种仅使用图像级别标签即可准确检测和分类CT图像中COVID-19的方法，避免昂贵的像素级标注。
• 在RT-PCR检测延迟或不可用时，支持疫情高峰期的快速临床决策。
• 通过减少对专家标注数据的依赖，实现AI辅助诊断的大规模部署。

提出的方法

• 采用弱监督深度学习方法，仅使用图像级别诊断标签（例如“COVID-19”或“非COVID-19”）进行训练。
• 采用带有注意力机制的深度神经网络架构，基于图像级别标签定位CT扫描中的可疑区域。
• 应用类似Grad-CAM的可视化技术生成热图预测，突出显示感染的肺部区域。
• 使用交叉熵损失和标准优化技术端到端训练模型。
• 利用在医学影像数据集上预训练的模型进行迁移学习，以提升泛化能力。
• 通过真实世界CT数据集的定性可视化和定量评估验证模型的鲁棒性。

实验结果

研究问题

• RQ1 仅使用图像级别标签，弱监督深度学习模型是否能在CT扫描中实现高检测与分类性能？
• RQ2 在无需像素级标注的情况下，该模型能否准确地定位肺部感染区域？
• RQ3 与完全监督基线相比，该模型在检测和分类准确率方面表现如何？
• RQ4 该模型在真实世界临床环境中，能否在不同患者群体和扫描协议下实现良好泛化？

主要发现

• 所提出的弱监督模型在独立测试集上实现了94.2%的分类准确率，表明在极少标注下仍具有强大性能。
• 该模型通过注意力图成功定位了肺部感染区域，可视化结果与放射科发现高度一致。
• 与完全监督方法相比，该方法显著降低了标注负担，从而加快了模型开发与部署速度。
• 该模型在不同CT扫描协议和患者人口统计特征下均保持了高鲁棒性和泛化能力。