论文标题
COVID-MOBILEXPERT：使用胸部X射线的Device Covid-19患者分类和随访
COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
论文作者
论文摘要
在Covid-19大流行期间，人们有新兴的需求，需要快速，专用和护理点的Covid-19患者处置技术，以优化资源利用率和临床工作流程。鉴于这种需求，我们提出了Covid-Mobilexpert：基于轻量的深神经网络（DNN）移动应用程序，可以使用胸部X射线（CXR）进行COVID-19 COVID-19病例筛选和放射轨迹预测。 We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and受过训练的轻型医学生（MS）网络，可在设备上进行Covid-19患者分类和随访。为了应对医学图像中非常相似和主导的前后和背景的挑战，我们为MS网络采用新颖的损失功能和培训方案来学习稳健的功能。我们证明了Covid-Mobilexpert通过具有不同的MS架构和调谐参数设置的广泛实验来快速部署的重要潜力。可从以下URL获得云和基于移动的模型的源代码：https：//github.com/xinli0928/covid-xray。
During the COVID-19 pandemic, there has been an emerging need for rapid, dedicated, and point-of-care COVID-19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for COVID-19 case screening and radiological trajectory prediction. We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network to perform on-device COVID-19 patient triage and follow-up. To tackle the challenge of vastly similar and dominant fore- and background in medical images, we employ novel loss functions and training schemes for the MS network to learn the robust features. We demonstrate the significant potential of COVID-MobileXpert for rapid deployment via extensive experiments with diverse MS architecture and tuning parameter settings. The source codes for cloud and mobile based models are available from the following url: https://github.com/xinli0928/COVID-Xray.
