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.

提出了一种多样教学方法 AD-MT，以应对当前半监督医学图像分割模型中存在的确认偏差问题。AD-MT 包括一个学生模型和两个非可训练的教师模型，通过交替地进行动量更新来促使教学多样性。通过随机切换周期、数据增强和补充数据批次，RPA 模块调度交替的多样化更新过程，以鼓励从不同教学角度进行多样化推理。而 CCM 模块基于熵集成策略，鼓励模型从教师模型的一致和冲突预测中学习。实验证明 AD-MT 在各半监督设置下对 2D 和 3D 医学分割基准的有效性和优越性。