Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.

翻译：对比学习在处理医学图像分割的标注稀缺问题方面展现出巨大潜力。现有方法通常假设标注与未标注的医学图像具有均衡的类别分布。然而，实际医疗图像数据普遍存在数据不平衡（即多类别标签不平衡），这自然会导致模糊的轮廓和罕见目标的错误标注。此外，所有负样本是否具有同等负向性仍不明确。本文提出ACTION——一种基于解剖感知对比蒸馏的半监督医学图像分割框架。具体而言，我们首先通过软标签标注负样本（而非采用正负样本对的二元监督）开发出迭代对比蒸馏算法；同时从随机选取的负样本集中捕获与正样本语义更相似的特征，以增强采样数据的多样性。其次，我们提出一个更关键的问题：能否真正通过处理不平衡样本来提升模型性能？因此，ACTION的核心创新在于：在最小化额外内存占用的前提下，学习跨整个数据集的全局语义关系与相邻像素间的局部解剖特征。训练过程中，我们通过主动采样稀疏的困难负像素引入解剖对比，从而生成更平滑的分割边界与更精准的预测。在两个基准数据集及不同未标注设置下的广泛实验表明，ACTION显著优于当前最先进的半监督方法。