Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians' trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (COVID-19) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatment. However, diagnostic uncertainty in a report is a challenging yet inevitable task for radiologists. In this paper, we investigate how Dropweights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solutions to improve the diagnostic performance of the human-machine combination using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is strongly correlated with the accuracy of the prediction. We believe that the availability of uncertainty-aware deep learning will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.
In REF , the authors investigated drop-weights based Bayesian Convolutional Neural Networks (BCNN) and its effect on improving the performance of the diagnostic of COVID-19 chest Xray.
Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection
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