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

Abstract

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.

COVID-19, SARS-CoV-2, On-device Machine Learning, Chest X-Ray (CXR)

References in corpus (13)

• Distilling the Knowledge in a Neural Network
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• Can AI help in screening Viral and COVID-19 pneumonia?
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• COVID-19 Image Data Collection
• Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
• Training Deep Neural Networks with 8-bit Floating Point Numbers
• Towards Understanding Knowledge Distillation
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
• On-Device Machine Learning: An Algorithms and Learning Theory Perspective
• A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
• A cascade network for Detecting COVID-19 using chest x-rays
• On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Cited by in corpus (7)

• Can AI help in screening Viral and COVID-19 pneumonia?
• Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification
• POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
• BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset
• Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
• Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
• Bias Field Poses a Threat to DNN-based X-Ray Recognition