Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China.
Department of Bioengineering, Stanford University, Stanford, CA, USA.
Med Image Anal. 2023 Aug;88:102880. doi: 10.1016/j.media.2023.102880. Epub 2023 Jun 28.
Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts, wherein the mean-teacher model, known as a milestone of perturbed consistency learning, commonly serves as a standard and simple baseline. Inherently, learning from consistency can be regarded as learning from stability under perturbations. Recent improvement leans toward more complex consistency learning frameworks, yet, little attention is paid to the consistency target selection. Considering that the ambiguous regions from unlabeled data contain more informative complementary clues, in this paper, we improve the mean-teacher model to a novel ambiguity-consensus mean-teacher (AC-MT) model. Particularly, we comprehensively introduce and benchmark a family of plug-and-play strategies for ambiguous target selection from the perspectives of entropy, model uncertainty and label noise self-identification, respectively. Then, the estimated ambiguity map is incorporated into the consistency loss to encourage consensus between the two models' predictions in these informative regions. In essence, our AC-MT aims to find out the most worthwhile voxel-wise targets from the unlabeled data, and the model especially learns from the perturbed stability of these informative regions. The proposed methods are extensively evaluated on left atrium segmentation and brain tumor segmentation. Encouragingly, our strategies bring substantial improvement over recent state-of-the-art methods. The ablation study further demonstrates our hypothesis and shows impressive results under various extreme annotation conditions.
半监督学习在医学图像分割中取得了巨大的进展，因为它有效地减轻了从专家那里获取大量注释的需求，其中均值教师模型作为扰动一致性学习的里程碑，通常作为标准和简单的基线。从一致性中学习可以被视为从扰动下的稳定性中学习。最近的改进倾向于更复杂的一致性学习框架，但对一致性目标选择的关注较少。考虑到未标记数据中的模糊区域包含更有信息量的互补线索，在本文中，我们将均值教师模型改进为一种新颖的模糊一致均值教师 (AC-MT) 模型。特别是，我们从熵、模型不确定性和标签噪声自识别的角度，全面介绍和基准测试了一系列用于从模糊目标中选择的即插即用策略。然后，将估计的模糊图纳入一致性损失中，以鼓励两个模型在这些信息丰富的区域中的预测达成一致。从本质上讲，我们的 AC-MT 旨在从未标记的数据中找出最有价值的体素级目标，并且模型特别从这些信息丰富的区域的扰动稳定性中学习。所提出的方法在左心房分割和脑肿瘤分割上进行了广泛的评估。令人鼓舞的是，我们的策略在最近的最先进方法上带来了实质性的改进。消融研究进一步验证了我们的假设，并在各种极端注释条件下取得了令人印象深刻的结果。
