Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection

TL;DR

This paper explores how Bayesian CNNs can estimate uncertainty in COVID-19 X-ray diagnosis, enhancing trust and performance in AI-assisted medical diagnosis.

Contribution

It demonstrates that drop-weights Bayesian CNNs effectively quantify uncertainty, correlating with prediction accuracy in COVID-19 detection from X-ray images.

Findings

Uncertainty estimates correlate with prediction accuracy.

Bayesian CNNs improve diagnostic confidence.

Enhanced trust in AI for clinical use.

Abstract

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 (SARS-CoV-2) 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 the report is a challenging and yet inevitable task for radiologist. In this paper, we investigate how drop-weights based Bayesian Convolutional Neural Networks (BCNN) can estimate uncertainty in Deep Learning solution to improve the diagnostic performance of the human-machine team using publicly available COVID-19 chest X-ray dataset and show that the uncertainty in prediction is highly correlates with accuracy of prediction. We believe that the availability of uncertainty-aware deep learning solution will enable a wider adoption of Artificial Intelligence (AI) in a clinical setting.