[论文解读] Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset

Lawrence Hall, Rahul Paul+2 more|arXiv (Cornell University)|Apr 5, 2020

COVID-19 diagnosis using AI被引用 7

一句话总结

本研究提出一种深度学习方法，利用仅102例COVID-19和102例非COVID肺炎病例的小型数据集，从胸部X光片中检测COVID-19。通过微调预训练的卷积神经网络，在未见的COVID-19病例上实现了100%的敏感性，AUC达到0.997，尽管存在数据有限和方法论局限性，但仍展现出巨大潜力。

ABSTRACT

Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them).

研究动机与目标

• 研究在诊断检测不足或延迟时，利用胸部X光片进行COVID-19早期检测的可行性。
• 评估深度学习模型在小型、不平衡的COVID-19与非COVID肺炎X光片数据集上的表现。
• 确定在训练数据有限的情况下，使用预训练CNN进行迁移学习是否能实现高诊断准确率。
• 评估模型在未见的COVID-19和非COVID肺炎病例上的泛化性能。

提出的方法

• 在经过筛选的102例COVID-19和102例非COVID肺炎胸部X光片数据集上，对预训练的深度卷积神经网络进行微调。
• 使用10折交叉验证评估模型在训练数据上的表现。
• 在一组独立的20例未见的COVID-19 X光片和4,171例非COVID肺炎X光片上测试模型。
• 采用迁移学习方法，将预训练网络适应于区分COVID-19与其他肺部感染的特定任务。
• 模型评估聚焦于敏感性、特异性和受试者工作特征曲线下面积（AUC）

实验结果

研究问题

• RQ1 仅使用少量胸部X光片图像，深度学习模型能否实现高诊断准确率用于COVID-19检测？
• RQ2 在小型、不平衡的COVID-19与非COVID肺炎X光片数据集上，迁移学习的表现如何？
• RQ3 该模型在未见的真实世界COVID-19病例及其他肺炎类型上的泛化性能如何？
• RQ4 不同测试集中的假阳性率与敏感性如何比较？

主要发现

• 训练集中全部102例COVID-19病例均被正确分类，敏感性达到100%。
• 在10折交叉验证中，模型在受试者工作特征曲线下面积（AUC）达到0.997。
• 在20例未见的COVID-19病例测试集中，所有病例均被正确分类，证实了外部验证中具有高敏感性。
• 超过95%的4,171例非COVID肺炎病例被正确分类，表明特异性较强。
• 整个测试集中共产生8例假阳性，突显了虽低但非零的假阳性率。