In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. This class of models involves assigning Dirichlet distributions as priors for segmentation labels, and enables a few distinct definitions of model uncertainties. Using the cardiac and prostate MRI images available in the Medical Segmentation Decathlon for validation, we found that Evidential Deep Learning models with U-Net backbones generally yielded superior correlations between prediction errors and uncertainties relative to the conventional baseline equipped with Shannon entropy measure, Monte-Carlo Dropout and Deep Ensemble methods. We also examined these models' effectiveness in active learning, finding that relative to the standard Shannon entropy-based sampling, they yielded higher point-biserial uncertainty-error correlations while attaining similar performances in Dice-Sorensen coefficients. These superior features of EDL models render them well-suited for segmentation tasks that warrant a critical sensitivity in detecting large model errors.

本研究针对生物医学图像分割中的不确定性量化问题，提出了一种基于证据深度学习的新框架，利用Dirichlet分布对分割标签进行建模。研究发现，证据深度学习模型在预测误差与不确定性之间的相关性方面优于传统方法，表明该模型在敏感的大模型误差检测任务上具有潜在优势。

生物医学分割中证据深度学习模型的误差与不确定性关联