Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural ne...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 2020-08, Vol.39 (8), p.2688-2700
Main Authors: Oh, Yujin, Park, Sangjoon, Ye, Jong Chul
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Betacoronavirus
Biomarkers
chest X-ray
classification
Computed tomography
Coronavirus Infections - diagnostic imaging
Coronaviruses
COVID-19
Datasets
Deep Learning
Diagnosis
Diseases
Humans
Image Interpretation, Computer-Assisted - methods
Image segmentation
Lung
Lung - diagnostic imaging
Neural networks
Pandemics
Pneumonia, Viral - diagnostic imaging
Radiographs
Radiography
Radiography, Thoracic - methods
saliency map
SARS-CoV-2
segmentation
Sensitivity
Statistical analysis
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2020.2993291