Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available.

中文翻译：

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引入高度不平衡数据的 GEV 激活函数来开发 COVID-19 诊断模型。

快速、准确的诊断对于高效、有效地控制目前正在扰乱全世界的 COVID-19 大流行至关重要。尽管 COVID-19 疫情普遍存在，但可用于开发自动诊断算法的公开诊断图像相对较少。当数据高度不平衡（一类中有很多情况而另一类只有少数情况）时，传统的深度学习方法常常会遇到困难；必须开发新方法来克服这一挑战。我们提出了一种基于极值理论的广义极值 (GEV) 分布的新型激活函数，当一个类别显着超过另一个类别时，该函数比传统的 sigmoid 激活函数提高了性能。我们在公开数据集上演示了所提出的激活函数，并在由 1,909 张健康胸部 X 光片和 84 张 COVID-19 X 光片组成的数据集上进行了外部验证。与 sigmoid 激活相比，所提出的方法在接收器操作特性下实现了改进的面积（DeLong 的 p 值 < 0.05）。我们的方法还在健康和肺炎与 COVID-19 X 射线数据集和一组计算机断层扫描图像上进行了验证，从而提高了灵敏度。所提出的 GEV 激活函数显着改进了之前用于二元分类的 sigmoid 激活函数。这种新模式预计将在对抗 COVID-19 和其他疾病方面发挥重要作用，但可用的培训案例相对较少。