Alternate Diverse Teaching for Semi-supervised Medical Image
  Segmentation
Zhen Zhao Zicheng Wang Longyue Wang Yixuan Yuan Luping Zhou
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.