Consistency learning is a central strategy to tackle unlabeled data in
semi-supervised medical image segmentation (SSMIS), which enforces the model to
produce consistent predictions under the perturbation. However, most current
approaches solely focus on utilizing a specific single perturbation, which can
only cope with limited cases, while employing multiple perturbations
simultaneously is hard to guarantee the quality of consistency learning. In
this paper, we propose an Adaptive Bidirectional Displacement (ABD) approach to
solve the above challenge. Specifically, we first design a bidirectional patch
displacement based on reliable prediction confidence for unlabeled data to
generate new samples, which can effectively suppress uncontrollable regions and
still retain the influence of input perturbations. Meanwhile, to enforce the
model to learn the potentially uncontrollable content, a bidirectional
displacement operation with inverse confidence is proposed for the labeled
images, which generates samples with more unreliable information to facilitate
model learning. Extensive experiments show that ABD achieves new
state-of-the-art performances for SSMIS, significantly improving different
baselines. Source code is available at this https URL

提出了一种自适应的双向位移方法 (ABD) 来解决半监督医学图像分割中不可标记数据一致性学习的挑战，包括可靠预测置信度的双向补丁位移生成新样本以有效抑制不可控区域和保留输入扰动的影响，以及通过逆置信度生成具有更不可靠信息的样本以促进模型学习。大量实验证明，ABD 在半监督医学图像分割方面取得了新的最佳性能，显著改进了不同基准模型。