Virtual versus reality: external validation of COVID-19 classifiers using XCAT phantoms for chest radiography

Abstract

Many published studies use deep learning models to predict COVID-19 from chest x-ray (CXR) images, often reporting high performances. However, the models do not generalize well on independent external testing. Common limitations include the lack of medical imaging data and disease labels, leading to training on small datasets or drawing classes from different institutions. To address these concerns, we designed an external validation study of deep learning classifiers for COVID-19 in CXR images including XCAT phantoms as well. We hypothesize that a simulated CXR image dataset obtained from the XCAT phantom allows for better control of the dataset including pixel-level ground truth. This setup allows for multiple advantages: First, we can validate the publicly available models using simulated chest x-rays. Secondly, we can also address clinically relevant questions with this setup such as effect of dose levels and sizeof COVID-19 pneumonia in performance of deep learning classifier. We

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