Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
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DOI：
10.1109/tmi.2020.2995508
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发表时间：
2020-08-01
影响因子：
10.6
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
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作者：
Ouyang, Xi;Huo, Jiayu;Shen, Dinggang
冠状病毒病(新冠肺炎)正在全球迅速传播，截至2020年4月9日，已在200多个国家和地区感染143.6万人。早期检测新冠肺炎对于为患者提供适当的医疗保健以及保护未感染人群至关重要。为此，我们开发了一个双采样注意网络来自动诊断新冠肺炎与胸部CT的社区获得性肺炎(CAP)。特别是，我们提出了一种新颖的在线注意模块，该模块具有3D卷积网络(CNN)，在做出诊断决策时专注于肺部的感染区域。值得注意的是，新冠肺炎和CAP之间存在感染区域大小的不平衡分布，部分原因是症状出现后新冠肺炎进展较快。因此，我们开发了一种双采样策略来缓解不平衡学习。我们的方法是基于来自8家医院的新冠肺炎最大的多中心CT数据进行评估(据我们所知)。在训练验证阶段，我们收集了1588名患者的2186张CT扫描，进行了5次交叉验证。在测试阶段，我们使用了另一个独立的大规模测试数据集，包括来自2057名患者的2796张CT扫描。实验结果表明，该算法对新冠肺炎图像的识别准确率为87.5%，灵敏度为86.9%，特异度为90.1%，F1评分为82.0%，受试者工作特征曲线下面积为0.944。凭借这种性能，建议的算法可能会潜在地帮助放射科医生从CAP中诊断新冠肺炎，特别是在新冠肺炎爆发的早期阶段。
The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.