Although the existing uncertainty-based semi-supervised medical segmentation
methods have achieved excellent performance, they usually only consider a
single uncertainty evaluation, which often fails to solve the problem related
to credibility completely. Therefore, based on the framework of evidential deep
learning, this paper integrates the evidential predictive results in the
cross-region of mixed and original samples to reallocate the confidence degree
and uncertainty measure of each voxel, which is realized by emphasizing
uncertain information of probability assignments fusion rule of traditional
evidence theory. Furthermore, we design a voxel-level asymptotic learning
strategy by introducing information entropy to combine with the fused
uncertainty measure to estimate voxel prediction more precisely. The model will
gradually pay attention to the prediction results with high uncertainty in the
learning process, to learn the features that are difficult to master. The
experimental results on LA, Pancreas-CT, ACDC and TBAD datasets demonstrate the
superior performance of our proposed method in comparison with the existing
state of the arts.

通过在混合和原始样本的跨区域整合证据预测结果，重分配每个体素的置信度和不确定度度量，结合信息熵设计了体素级渐进学习策略以更加准确地估计体素的预测结果。实验结果表明，我们提出的方法在 LA、胰腺 CT、ACDC 和 TBAD 数据集上相对于现有技术取得了卓越的性能。