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：对比学习在医学图像分割中的标注稀缺问题上展现了巨大潜力。现有方法通常假设标注与未标注医学图像具有均衡的类别分布，然而实际医学图像数据普遍存在类别不均衡现象（即多类标签分布失衡），这自然会导致模糊的边界轮廓，且常将稀有目标错误标记。此外，所有负样本是否具有同等负性尚不明确。本文提出ACTION——一种基于解剖感知对比蒸馏框架的半监督医学图像分割方法。具体而言，我们首先通过软标签标记负样本（而非对正负样本对采用二元监督）开发了迭代式对比蒸馏算法，同时从随机选取的负样本集合中捕获比正样本更具语义相似性的特征，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：是否真正能通过处理不均衡样本来提升性能？因此，ACTION的核心创新在于以最小化额外内存开销学习整个数据集间的全局语义关系与相邻像素间的局部解剖特征。在训练过程中，我们通过主动采样稀疏的硬负像素引入解剖对比，从而生成更平滑的分割边界与更准确的预测。在两种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。