Semi-supervised segmentation presents a promising approach for large-scale
medical image analysis, effectively reducing annotation burdens while achieving
comparable performance. This methodology holds substantial potential for
streamlining the segmentation process and enhancing its feasibility within
clinical settings for translational investigations. While cross-supervised
training, based on distinct co-training sub-networks, has become a prevalent
paradigm for this task, addressing critical issues such as predication
disagreement and label-noise suppression requires further attention and
progress in cross-supervised training. In this paper, we introduce an
Evidential Tri-Branch Consistency learning framework (ETC-Net) for
semi-supervised medical image segmentation. ETC-Net employs three branches: an
evidential conservative branch, an evidential progressive branch, and an
evidential fusion branch. The first two branches exhibit complementary
characteristics, allowing them to address prediction diversity and enhance
training stability. We also integrate uncertainty estimation from the
evidential learning into cross-supervised training, mitigating the negative
impact of erroneous supervision signals. Additionally, the evidential fusion
branch capitalizes on the complementary attributes of the first two branches
and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by
more reliable and accurate pseudo-labels of unlabeled data. Extensive
experiments conducted on LA, Pancreas-CT, and ACDC datasets demonstrate that
ETC-Net surpasses other state-of-the-art methods for semi-supervised
segmentation. The code will be made available in the near future at
this https URL

介绍了一种基于 Evidential Tri-Branch Consistency 学习框架（ETC-Net）的半监督医学图像分割方法，通过三个分支来解决预测多样性和训练稳定性，并结合来自证据学习的不确定性估计来抑制错误监督信号的负面影响，实验结果表明 ETC-Net 在多个数据集上超越了其他最先进的半监督分割方法。

一种证据增强的三支一致性学习方法用于半监督医学图像分割

An Evidential-enhanced Tri-Branch Consistency Learning Method for  Semi-supervised Medical Image Segmentation

Semi-supervised medical image segmentation offers a promising solution for
large-scale medical image analysis by significantly reducing the annotation
burden while achieving comparable performance. Employing this method exhibits a
high degree of potential for optimizing the segmentation process and increasing
its feasibility in clinical settings during translational investigations.
Recently, cross-supervised training based on different co-training sub-networks
has become a standard paradigm for this task. Still, the critical issues of
sub-network disagreement and label-noise suppression require further attention
and progress in cross-supervised training. This paper proposes a
cross-supervised learning framework based on dual classifiers (DC-Net),
including an evidential classifier and a vanilla classifier. The two
classifiers exhibit complementary characteristics, enabling them to handle
disagreement effectively and generate more robust and accurate pseudo-labels
for unlabeled data. We also incorporate the uncertainty estimation from the
evidential classifier into cross-supervised training to alleviate the negative
effect of the error supervision signal. The extensive experiments on LA and
Pancreas-CT dataset illustrate that DC-Net outperforms other state-of-the-art
methods for semi-supervised segmentation. The code will be released soon.

本文提出一种基于双分类器（DC-Net）的交叉监督学习框架，包括证明分类器和香草分类器，各具特色，能够有效处理分歧，并为无标签数据生成更稳健和准确的伪标签，同时将证明分类器的不确定估计结合到交叉监督训练中以减轻错误监督信号的负面影响，试验证明该方法优于其他半监督分割的最新研究成果。