Semi-supervised medical image segmentation (SSMIS) has been demonstrated the potential to mitigate the issue of limited medical labeled data. However, confirmation and cognitive biases may affect the prevalent teacher-student based SSMIS methods due to erroneous pseudo-labels. To tackle this challenge, we improve the mean teacher approach and propose the Students Discrepancy-Informed Correction Learning (SDCL) framework that includes two students and one non-trainable teacher, which utilizes the segmentation difference between the two students to guide the self-correcting learning. The essence of SDCL is to identify the areas of segmentation discrepancy as the potential bias areas, and then encourage the model to review the correct cognition and rectify their own biases in these areas. To facilitate the bias correction learning with continuous review and rectification, two correction loss functions are employed to minimize the correct segmentation voxel distance and maximize the erroneous segmentation voxel entropy. We conducted experiments on three public medical image datasets: two 3D datasets (CT and MRI) and one 2D dataset (MRI). The results show that our SDCL surpasses the current State-of-the-Art (SOTA) methods by 2.57\%, 3.04\%, and 2.34\% in the Dice score on the Pancreas, LA, and ACDC datasets, respectively. In addition, the accuracy of our method is very close to the fully supervised method on the ACDC dataset, and even exceeds the fully supervised method on the Pancreas and LA dataset. (Code available at \url{https://github.com/pascalcpp/SDCL}).

翻译：半监督医学图像分割（SSMIS）已被证明具有缓解医学标注数据有限问题的潜力。然而，在基于师生架构的主流SSMIS方法中，错误的伪标签可能导致确认偏倚与认知偏倚。为应对这一挑战，我们在均值教师方法基础上进行改进，提出了学生差异感知修正学习（SDCL）框架。该框架包含两个学生网络与一个不可训练的教师网络，通过利用两个学生网络间的分割差异来引导自修正学习。SDCL的核心思想是将分割差异区域识别为潜在偏倚区域，进而促使模型在这些区域中重新审视正确认知并修正自身偏倚。为实现持续审视与修正的偏倚校正学习，我们采用两种修正损失函数：最小化正确分割体素距离与最大化错误分割体素熵。我们在三个公开医学图像数据集上进行了实验：两个3D数据集（CT与MRI）和一个2D数据集（MRI）。结果表明，我们的SDCL方法在胰腺、左心房和ACDC数据集上的Dice分数分别超越当前最优方法2.57%、3.04%和2.34%。此外，本方法在ACDC数据集上的准确率已接近全监督方法，在胰腺和左心房数据集上甚至超越了全监督方法。（代码发布于\url{https://github.com/pascalcpp/SDCL}）。