用不确定性量化提升3D肺部CT诊断新冠肺炎的可信度
Enhancing Diagnostic in 3D COVID-19 Pneumonia CT-scans through Explainable Uncertainty Bayesian Quantification
- 采用贝叶斯神经网络结合归一化流技术，输出预测置信度
- 轻量级模型经调参后达到96%准确率，且不确定性估计更可靠
- 通过SHAP可视化解释模型决策，适合临床医生辅助诊断
在医学影像分析领域，精准分类3D CT扫描中的新冠肺炎肺炎仍具挑战性。尽管确定性神经网络已取得良好效果，但仅提供点估计，难以支持临床决策。本文探索使用贝叶斯神经网络对3D CT扫描进行新冠肺炎肺炎分类，提供预测不确定性。对比确定性网络及其贝叶斯版本，发现经过充分超参数调优的轻量级架构可实现96%的最高准确率。此外，基于乘法归一化流技术的贝叶斯模型保持相近性能，并具备校准后的不确定性估计。最后，我们开发了一种3D可视化方法，基于SHAP值解释神经网络输出。结果表明，可解释性与不确定性量化结合，将显著提升医学影像分析中的临床决策能力，助力新冠肺炎肺炎的诊断与治疗优化。
原文摘要 · Abstract (English)
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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