Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification

TL;DR

This paper demonstrates that Bayesian neural networks with uncertainty quantification and explainability improve COVID-19 pneumonia diagnosis accuracy in 3D CT scans, aiding clinical decision-making.

Contribution

It introduces a Bayesian approach with uncertainty estimation and a 3D visualization method to enhance diagnosis and interpretability of neural networks in COVID-19 CT analysis.

Findings

Lightweight architectures achieve 96% accuracy.

Bayesian models maintain performance with calibrated uncertainties.

Explainability via SHAP values aids clinical interpretation.

Abstract

Accurately classifying COVID-19 pneumonia in 3D CT scans remains a significant challenge in the field of medical image analysis. Although deterministic neural networks have shown promising results in this area, they provide only point estimates outputs yielding poor diagnostic in clinical decision-making. In this paper, we explore the use of Bayesian neural networks for classifying COVID-19 pneumonia in 3D CT scans providing uncertainties in their predictions. We compare deterministic networks and their Bayesian counterpart, enhancing the decision-making accuracy under uncertainty information. Remarkably, our findings reveal that lightweight architectures achieve the highest accuracy of 96\% after developing extensive hyperparameter tuning. Furthermore, the Bayesian counterpart of these architectures via Multiplied Normalizing Flow technique kept a similar performance along with calibrated uncertainty estimates. Finally, we have developed a 3D-visualization approach to explain the neural network outcomes based on SHAP values. We conclude that explainability along with uncertainty quantification will offer better clinical decisions in medical image analysis, contributing to ongoing efforts for improving the diagnosis and treatment of COVID-19 pneumonia.