For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth label, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical features and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group sampling theory in medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation, and show that certain variance-reduction techniques are particularly beneficial in medical image segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on three benchmark datasets with different label settings, and our methods consistently outperform state-of-the-art semi- and fully-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing medical image analysis tasks.

翻译：对于医学图像分割，对比学习通过对比语义相似与不相似的样本对来提升视觉表示质量，已成为主流实践。这一方法源于以下观察：无需访问真实标签，若能采样到解剖特征真正不相似的负样本，可显著提升性能。然而在现实中，这些样本可能来自相似的解剖特征，模型难以区分少数尾部类样本，导致尾部类别更易被错误分类，这两者通常引发模型崩溃。本文提出ARCO——一种基于分层组采样理论的半监督对比学习框架，应用于医学图像分割。具体而言，我们首先通过方差缩减估计概念构建ARCO，并证明某些方差缩减技术对标签极有限的医学图像分割任务尤为有益。此外，我们从理论上证明这些采样技术在方差缩减中具有普适性。最后，我们在三个不同标签设置的基准数据集上通过实验验证了所提方法，其结果持续优于最先进的半监督与全监督方法。同时，我们将这些采样技术融入对比学习框架，展现出相较先前方法的显著提升。我们相信，通过量化当前自监督目标在完成医学图像分析任务时的局限性，本工作是迈向半监督医学图像分割的重要一步。