Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
This addresses the challenge of rapid and accurate COVID-19 diagnosis for healthcare systems during a pandemic, though it is incremental in method.
The authors tackled the problem of limited training data for COVID-19 diagnosis from chest X-rays by proposing a patch-based convolutional neural network, achieving state-of-the-art performance and providing clinically interpretable saliency maps.
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.