  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.
