论文标题
贝叶斯神经网络用于成像生物标志物的不确定性估计
Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers
论文作者
论文摘要
图像分割使可以从扫描中提取定量措施，这些措施可以用作疾病的成像生物标志物。但是，分割质量在整个扫描中可能有很大差异，因此在生物标志物的后续统计分析中产生了不忠的估计。核心问题是分割和生物标志物分析是独立执行的。我们建议将分割不确定性传播到统计分析，以说明分割置信度的变化。为此，我们评估了四个贝叶斯神经网络，以从后验分布中采样并估计不确定性。然后，我们将置信度度量分配给生物标志物，并提出统计模型，以集成小组分析和疾病分类。我们分割糖尿病患者肝脏的结果清楚地证明了在统计推断中整合生物标志物不确定性的改善。
Image segmentation enables to extract quantitative measures from scans that can serve as imaging biomarkers for diseases. However, segmentation quality can vary substantially across scans, and therefore yield unfaithful estimates in the follow-up statistical analysis of biomarkers. The core problem is that segmentation and biomarker analysis are performed independently. We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. To this end, we evaluate four Bayesian neural networks to sample from the posterior distribution and estimate the uncertainty. We then assign confidence measures to the biomarker and propose statistical models for its integration in group analysis and disease classification. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference.
