Effective and reliable screening of patients via Computer-Aided Diagnosis can play a crucial part in the battle against COVID-19. Most of the existing works focus on developing sophisticated methods yielding high detection performance, yet not addressing the issue of predictive uncertainty. In this work, we introduce uncertainty estimation to detect confusing cases for expert referral to address the unreliability of state-of-the-art (SOTA) DNNs on COVID-19 detection. To the best of our knowledge, we are the first to address this issue on the COVID-19 detection problem. In this work, we investigate a number of SOTA uncertainty estimation methods on publicly available COVID dataset and present our experimental findings. In collaboration with medical professionals, we further validate the results to ensure the viability of the best performing method in clinical practice.

翻译：通过计算机辅助诊断对病人进行有效和可靠的筛查,可在打击COVID-19的斗争中发挥关键作用。大多数现有工作侧重于开发尖端方法,产生高检测性能,但并未解决预测性不确定性问题。在这项工作中,我们引入不确定性估算,以发现难以解答的病例,供专家转介,以解决在COVID-19检测方面最先进的(SOTA)DNs不可靠的问题。据我们所知,我们首先处理COVID-19检测问题。在这项工作中,我们调查了一些关于公开提供的COVID数据集的SOTA不确定性估算方法,并介绍我们的实验结果。我们与医疗专业人员合作,进一步验证这些结果,以确保临床实践中最佳运行方法的可行性。