Semi-supervised medical image segmentation studies have shown promise in training models with limited labeled data. However, current dominant teacher-student based approaches can suffer from the confirmation bias. To address this challenge, we propose AD-MT, an alternate diverse teaching approach in a teacher-student framework. It involves a single student model and two non-trainable teacher models that are momentum-updated periodically and randomly in an alternate fashion. To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM). The RPA schedules the alternating diverse updating process with complementary data batches, distinct data augmentation, and random switching periods to encourage diverse reasoning from different teaching perspectives. The CCM employs an entropy-based ensembling strategy to encourage the model to learn from both the consistent and conflicting predictions between the teachers. Experimental results demonstrate the effectiveness and superiority of our AD-MT on the 2D and 3D medical segmentation benchmarks across various semi-supervised settings.

翻译：半监督医学图像分割研究已展现出在有限标注数据下训练模型的潜力。然而，当前主流的基于师生框架的方法易受确认偏误的影响。为应对这一挑战，我们提出AD-MT——一种在师生框架中采用的交替多样性教学方法。该方法包含一个学生模型和两个非可训练的教师模型，这些教师模型以交替方式定期且随机地进行动量更新。为缓解多样性监督带来的确认偏误，AD-MT的核心在于两个提出的模块：随机周期交替更新模块与冲突对抗模块。RPA模块通过互补数据批次、差异化数据增强及随机切换周期来调度交替多样性更新过程，从而激励从不同教学视角进行多样性推理。CCM模块采用基于熵的集成策略，促使模型同时学习教师间一致与冲突的预测结果。实验结果表明，我们的AD-MT方法在多种半监督设置下的二维与三维医学分割基准数据集上均表现出有效性和优越性。