Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large number of unlabeled images for network training, is beneficial for relieving the burden of data annotation in medical image segmentation. According to the experience of medical imaging experts, local attributes such as texture, luster and smoothness are very important factors for identifying target objects like lesions and polyps in medical images. Motivated by this, we propose a cross-level contrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation. Compared to existing image-wise, patch-wise and point-wise contrastive learning algorithms, our devised method is capable of exploring more complex similarity cues, namely the relational characteristics between global and local patch-wise representations. Additionally, for fully making use of cross-level semantic relations, we devise a novel consistency constraint that compares the predictions of patches against those of the full image. With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance on two medical image datasets for polyp and skin lesion segmentation respectively. Code of our approach is available.

翻译：半监督的学习(SSL)旨在利用少数贴标签的图像和大量未贴标签的图像进行网络培训,它有助于减轻医疗图像分割中数据注释的负担。根据医学成像专家的经验,当地特征,如纹理、光滑和光滑等,是确定医学图像中损伤和聚光体等目标对象的非常重要的因素。我们为此提出一个跨层次对比学习计划,以提高半监督的医学图像分割中地方特征的体现能力。与现有图像、补丁和近似对比性学习算法相比,我们设计的方法能够探索更复杂的相似性提示,即全球和本地近似性表述之间的关系特征。此外,为了充分利用跨层次的语义关系,我们设计了一个新颖的一致性限制,将补丁的预测与完整图像的预测进行比较。通过跨层次的对比学习和一致性制约,可以有效地探索未贴标签的数据,以改进我们两种医学图像分割方法的分解性性功能,分别用于聚谱和皮质分解。