COVID MobileXpert: On Device COVID 19 Patient Triage and Follow up using Chest X rays
During the COVID 19 pandemic, there has been an emerging need for rapid, dedicated, and point of care COVID19 patient disposition techniques to optimize resource utilization and clinical workflow. In view of this need, we present COVIDMobileXpert: 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 implementations are available from the following url: https:/github.com/xinli0928/COVID Xray.
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)