通过可解释的不确定性贝叶斯量化增强3D COVID-19肺炎CT扫描的诊断
Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification
AI总结：
本文提出利用贝叶斯神经网络对3D CT扫描中的COVID-19肺炎进行分类，通过不确定性量化与SHAP可解释性提升诊断准确性，轻量级架构达到96%准确率。
摘要：
在3D CT扫描中准确分类COVID-19肺炎仍然是医学图像分析领域的一个重大挑战。尽管确定性神经网络在该领域已显示出有前景的结果，但它们仅提供点估计输出，在临床决策中导致诊断效果不佳。本文探索使用贝叶斯神经网络对3D CT扫描中的COVID-19肺炎进行分类，并在预测中提供不确定性。我们比较了确定性网络及其贝叶斯对应物，利用不确定性信息提高决策准确性。值得注意的是，我们的研究发现，经过广泛的超参数调优后，轻量级架构达到了96%的最高准确率。此外，通过乘性归一化流技术实现的这些架构的贝叶斯对应物保持了相似的性能，并提供了校准的不确定性估计。最后，我们开发了一种基于SHAP值的3D可视化方法来解释神经网络输出。我们得出结论，可解释性与不确定性量化相结合将为医学图像分析提供更好的临床决策，有助于持续改进COVID-19肺炎的诊断和治疗。
英文摘要：
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.
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