Although current semi-supervised medical segmentation methods can achieve
decent performance, they are still affected by the uncertainty in unlabeled
data and model predictions, and there is currently a lack of effective
strategies that can explore the uncertain aspects of both simultaneously. To
address the aforementioned issues, we propose Evidential Prototype Learning
(EPL), which utilizes an extended probabilistic framework to effectively fuse
voxel probability predictions from different sources and achieves prototype
fusion utilization of labeled and unlabeled data under a generalized evidential
framework, leveraging voxel-level dual uncertainty masking. The uncertainty not
only enables the model to self-correct predictions but also improves the guided
learning process with pseudo-labels and is able to feed back into the
construction of hidden features. The method proposed in this paper has been
experimented on LA, Pancreas-CT and TBAD datasets, achieving the
state-of-the-art performance in three different labeled ratios, which strongly
demonstrates the effectiveness of our strategy.

本研究提出了 Evidential Prototype Learning (EPL) 方法，利用扩展的概率框架有效融合来自不同来源的体素概率预测，并在广义证据框架下实现标记和未标记数据的样本融合利用，利用体素级双不确定性掩膜。该方法在 LA、胰腺 CT 和 TBAD 数据集上进行了实验，以三种不同标记比率实现了最先进的性能，充分证明了我们的策略的有效性。