  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.
