Abstract
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.
Abstract (translated)
半监督医学图像分割提供了一个有前途的解决方案，通过显著减少标注负担而实现类似的性能。使用这种方法可以展示高度的潜力，以优化分割过程并增加在临床实验期间 Translational 研究期间的实践可行性。最近，基于不同的协同训练子网络的交叉监督训练已经成为该任务的标准范式。然而，子网络不同意和标签噪声抑制等关键问题需要进一步的关注和进展的交叉监督训练。本文提出了基于双重分类器(DC-Net)的交叉监督学习框架，包括证据分类器和无分类分类器。两个分类器具有互补的特征，使他们能够有效地处理不同意并生成未标记数据更为稳健和准确的伪标签。我们还将证据分类器的不确定估计引入交叉监督训练，以减轻错误监督信号的负面影响。在LA和肝脏CT数据集上的广泛实验表明，DC-Net在半监督分割方面优于其他先进的方法。代码将很快发布。
URL
https://arxiv.org/abs/2305.16216
