Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

中文翻译：

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使用有限的训练数据集的CXR上的深度学习COVID-19功能。

在COVID-19的全球大流行中，使用人工智能分析胸部X射线（CXR）图像以进行COVID-19诊断和患者分诊变得越来越重要。不幸的是，由于COVID-19大流行的紧急性质，难以对用于深度神经网络训练的CXR数据集进行系统收集。为了解决这个问题，在这里我们提出了一种基于补丁的卷积神经网络方法，该方法具有相对较少的可训练参数，可用于COVID-19诊断。我们对CXR射线照片的潜在成像生物标志物的统计分析启发了提出的方法。实验结果表明，我们的方法达到了最先进的性能，并提供了可临床解释的显着性图，可用于COVID-19诊断和患者分类。