论文标题
使用有限的培训数据集，深度学习COVID-19在CXR上的功能
Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
论文作者
论文摘要
在Covid-19的全球大流行中，使用人工智能来分析胸部X射线（CXR）图像进行COVID-19诊断和患者分类变得很重要。不幸的是，由于COVID-19大流行的新兴性质，很难将CXR数据集的系统收集用于深神经网络训练。为了解决这个问题，我们在这里提出了一种基于斑块的卷积神经网络方法，其可训练的参数相对较少，用于COVID-19诊断。提出的方法的灵感来自我们对CXR X光片的潜在成像生物标志物的统计分析。实验结果表明，我们的方法可实现最先进的性能，并提供临床上可解释的显着图，这对于COVID-19诊断和患者分类很有用。
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 the 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.
