Evidential Deep Learning能更准确预测医学图像分割中的错误，提升模型可靠性。
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
- 用狄利克雷先验建模分割不确定性，量化多种误差类型。
- 在心脏和前列腺MRI数据上，误差与不确定性的相关性优于传统方法。
- 适合对错误敏感的医学分割任务，尤其适用于主动学习场景。
本文研究了证据深度学习（Evidential Deep Learning, EDL）在生物医学图像分割中不确定性量化的效果。该方法为分割标签设置狄利克雷先验分布，可定义多种模型不确定性。基于医学分割挑战赛（Medical Segmentation Decathlon）中的心脏和前列腺MRI数据进行验证，结果表明：采用U-Net主干网络的EDL模型在预测误差与不确定性之间的相关性上显著优于使用香农熵、蒙特卡洛丢弃和深度集成等传统基线方法。此外，在主动学习中，相比标准香农熵采样，EDL模型在保持相似Dice-Sorensen系数的同时，实现了更高的点双列相关系数（point-biserial correlation）来衡量不确定性与误差的关系。这些优势使EDL模型特别适用于需要高敏感度检测大误差的医学图像分割任务。
原文摘要 · Abstract (English)
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.
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