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 the 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.
@article{arxiv.2004.05758,
title = {Deep Learning COVID-19 Features on CXR using Limited Training Data Sets},
author = {Yujin Oh and Sangjoon Park and Jong Chul Ye},
journal= {arXiv preprint arXiv:2004.05758},
year = {2020}
}
Comments
Accepted for IEEE Trans. on Medical Imaging Special Issue on Imaging-based Diagnosis of COVID-19