Teacher-student frameworks have emerged as a leading approach in semi-supervised medical image segmentation, demonstrating strong performance across various tasks. However, the learning effects are still limited by the strong correlation and unreliable knowledge transfer process between teacher and student networks. To overcome this limitation, we introduce a novel switching Dual-Student architecture that strategically selects the most reliable student at each iteration to enhance dual-student collaboration and prevent error reinforcement. We also introduce a strategy of Loss-Aware Exponential Moving Average to dynamically ensure that the teacher absorbs meaningful information from students, improving the quality of pseudo-labels. Our plug-and-play framework is extensively evaluated on 3D medical image segmentation datasets, where it outperforms state-of-the-art semi-supervised methods, demonstrating its effectiveness in improving segmentation accuracy under limited supervision.

翻译：教师-学生框架已成为半监督医学图像分割领域的主流方法，在各种任务中展现出优异的性能。然而，学习效果仍受限于教师网络与学生网络之间的强相关性及不可靠的知识迁移过程。为克服这一局限，我们提出一种新颖的切换式双学生架构，该架构在每次迭代中策略性地选择最可靠的学生模型，以增强双学生协作并避免错误强化。我们还引入损失感知指数移动平均策略，动态确保教师模型从学生处吸收有效信息，从而提升伪标签质量。我们的即插即用框架在三维医学图像分割数据集上进行了广泛评估，其性能优于当前最先进的半监督方法，证明了该框架在有限监督条件下提升分割精度的有效性。