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7 April 2023 Bayesian neural networks for severity assessment of COVID-19 pneumonia from chest x-ray using a multi-reader and binational dataset
Mohammadreza Zandehshahvar, Marly van Assen, Eun Young Kim, Yashar Kiarashi, Vikranth Keerthipati, Arthur E. Stillman, Peter Filev, Amir H. Davarpanah, Eugene A. Berkowitz, Stefan Tigges, Scott J. Lee, Brianna L. Vey, Carlo De Cecco, Ali Adibi
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Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650G (2023) https://doi.org/10.1117/12.2653346
Event: SPIE Medical Imaging, 2023, San Diego, California, United States
Abstract
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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Mohammadreza Zandehshahvar, Marly van Assen, Eun Young Kim, Yashar Kiarashi, Vikranth Keerthipati, Arthur E. Stillman, Peter Filev, Amir H. Davarpanah, Eugene A. Berkowitz, Stefan Tigges, Scott J. Lee, Brianna L. Vey, Carlo De Cecco, and Ali Adibi "Bayesian neural networks for severity assessment of COVID-19 pneumonia from chest x-ray using a multi-reader and binational dataset", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650G (7 April 2023); https://doi.org/10.1117/12.2653346
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KEYWORDS
Chest imaging
Neural networks
Data modeling
Lung
Machine learning
Monte Carlo methods
Radiology
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