We present a new approach based on Bayesian neural networks (BNNs) for severity assessment of lung diseases using chest X-rays (CXRs). In contrast to reqular NNs, our model can provide uncertainty of the prediction for an input CXR which is crucial for clinical implementation of machine learning-assisted tools in radiology. With no loss of generality, we apply this method for severity assessment of COVID-19 pneumonia using multi-reader datasets from the USA and Korea. Our results show that the BNN can classify COVID-19 pneumonia with performance comparable to human experts while providing prediction uncertainty. We also compare the uncertainty of the model over different severity classes with inter-reader variability among the radiologists.
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