论文标题
评论X射线图像自动检测方法的评估评估
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
论文作者
论文摘要
在本文中，我们比较和评估了最近文献中X射线图像的自动Covid-19诊断的不同测试方案。我们表明，使用不包含大多数肺部的X射线图像可以获得类似的结果。我们能够通过将X射线扫描中心变成黑色并仅在图像的外部训练分类器，从而从图像中清除肺部。因此，我们推断出识别的几种测试协议是不公平的，并且神经网络是数据集中的学习模式，与COVID-19的存在无关。最后，我们表明创建公平的测试协议是一项具有挑战性的任务，我们提供了一种衡量特定测试协议的公平程度的方法。在将来的研究中，我们建议使用我们的工具检查测试协议的公平性，并鼓励研究人员寻找比我们建议的技术更好的技术。
In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature. We show that similar results can be obtained using X-Ray images that do not contain most of the lungs. We are able to remove the lungs from the images by turning to black the center of the X-Ray scan and training our classifiers only on the outer part of the images. Hence, we deduce that several testing protocols for the recognition are not fair and that the neural networks are learning patterns in the dataset that are not correlated to the presence of COVID-19. Finally, we show that creating a fair testing protocol is a challenging task, and we provide a method to measure how fair a specific testing protocol is. In the future research we suggest to check the fairness of a testing protocol using our tools and we encourage researchers to look for better techniques than the ones that we propose.
