Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by existing studies. Meanwhile, the limited quantity of labeled data for training often proves inadequate for formulating intra-class compactness and inter-class discrepancy of pseudo labels. To address these issues, we propose a self-aware and cross-sample prototypical learning method (SCP-Net) to enhance the diversity of prediction in consistency learning by utilizing a broader range of semantic information derived from multiple inputs. Furthermore, we introduce a self-aware consistency learning method that exploits unlabeled data to improve the compactness of pseudo labels within each class. Moreover, a dual loss re-weighting method is integrated into the cross-sample prototypical consistency learning method to improve the reliability and stability of our model. Extensive experiments on ACDC dataset and PROMISE12 dataset validate that SCP-Net outperforms other state-of-the-art semi-supervised segmentation methods and achieves significant performance gains compared to the limited supervised training. Our code will come soon.

翻译：一致性学习在半监督医学图像分割中发挥着关键作用，因为它能够在充分利用大量未标注数据的同时，有效利用有限的标注数据。然而，预测多样性和训练稳定性对一致性学习的效果与效率构成挑战，而现有研究往往忽视了这些问题。同时，标注数据数量有限，难以实现伪标签的类内紧凑性和类间区分性。为解决这些问题，我们提出一种自感知与跨样本原型学习方法（SCP-Net），通过利用从多个输入中获取的更广泛语义信息，增强一致性学习中预测的多样性。此外，我们引入一种自感知一致性学习方法，利用未标注数据提高各类别内伪标签的紧凑性。进一步地，我们在跨样本原型一致性学习方法中集成了一种双损失重加权方法，以提升模型的可靠性和稳定性。在ACDC数据集和PROMISE12数据集上的大量实验表明，SCP-Net优于其他最先进的半监督分割方法，并且相较于仅依赖有限监督训练的方法实现了显著的性能提升。我们的代码即将发布。