论文标题
COVID-19的超声诊断：鲁棒性和解释性
Ultrasound Diagnosis of COVID-19: Robustness and Explainability
论文作者
论文摘要
在护理点进行COVID-19的诊断对于全球大流行的遏制至关重要。护理点超声（POCUS）提供了以可重复且具有成本效益的方式检测患者的肺部快速图像。先前的工作已使用Pocus视频的公共数据集来训练AI模型以获得高灵敏度的诊断。由于应用高赌注，我们提出了使用可靠和可解释的技术的使用。我们通过实验证明，强大的模型具有更稳定的预测，并提供了改善的解释性。基于对抗性扰动的对比解释的框架用于解释与人类视觉感知一致的模型预测。
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an AI model for diagnosis that obtains high sensitivity. Due to the high stakes application we propose the use of robust and explainable techniques. We demonstrate experimentally that robust models have more stable predictions and offer improved interpretability. A framework of contrastive explanations based on adversarial perturbations is used to explain model predictions that aligns with human visual perception.
