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.

翻译：半监督医学图像分割通过显著减轻标注负担同时实现可比较的性能，为大规模医学图像分析提供了有前景的解决方案。采用该方法在优化分割过程及提高其在转化研究临床场景中的可行性方面展现出巨大潜力。近年来，基于不同协同训练子网络的交叉监督训练已成为该任务的标准范式。然而，子网络分歧与标签噪声抑制等关键问题仍需在交叉监督训练中进一步关注与突破。本文提出基于双分类器的交叉监督学习框架（DC-Net），包含证据分类器与普通分类器。这两个分类器具有互补特性，能够有效处理分歧，为未标注数据生成更鲁棒且精确的伪标签。我们还融合证据分类器的不确定性估计至交叉监督训练中，以减轻错误监督信号的负面影响。在LA和Pancreas-CT数据集上的大量实验表明，DC-Net在半监督分割任务中优于其他先进方法。代码即将开源。