# COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

arXiv: https://arxiv.org/abs/2004.03042 · HTML: https://mlsanity.com/paper/2004.03042/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis](https://mlsanity.com/paper/2106.09759/) (2021)
- [MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images](https://mlsanity.com/paper/2108.08775/) (2021)
- [Classification of COVID-19 from CXR Images in a 15-class Scenario: an Attempt to Avoid Bias in the System](https://mlsanity.com/paper/2109.12453/) (2021)

## Abstract (arXiv)

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.