Recent studies on contrastive learning have achieved remarkable performance solely by leveraging few labels in the context of medical image segmentation. Existing methods mainly focus on instance discrimination and invariant mapping. However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. This motivates us to seek a principled approach for strategically making use of the dataset itself to discover similar yet distinct samples from different anatomical views. In this paper, we introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA), and make three contributions. First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features, mainly due to lacking the supervision signal. We show two simple solutions towards learning invariances - through the use of stronger data augmentations and nearest neighbors. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features in an unsupervised manner. Lastly, we both empirically and theoretically, demonstrate the efficacy of our MONA on three benchmark datasets with different labeled settings, achieving new state-of-the-art under different labeled semi-supervised settings

翻译：近年来，基于对比学习的研究仅通过利用少量标注即在医学图像分割领域取得了显著性能。现有方法主要聚焦于实例判别与不变映射，但通常存在三个普遍问题：（1）长尾分布：医学图像数据通常呈现隐式长尾类分布，盲目利用所有像素进行训练会导致数据不平衡问题，并造成性能下降；（2）一致性：由于不同解剖特征间的类内差异，分割模型是否学习了有意义且一致的解剖特征仍不明确；（3）多样性：整个数据集内切片间的相关性尚未得到充分关注。这促使我们探索一种系统化方法，策略性地利用数据集本身从不同解剖视角发现相似但不同的样本。本文提出一种新型半监督2D医学图像分割框架——Mine yOur owN Anatomy（MONA），并做出三项贡献。首先，已有工作认为每个像素对模型训练同等重要，但实验表明，仅凭此点难以定义有意义的解剖特征，主要原因是缺乏监督信号。我们展示了两种通过使用更强数据增强和最近邻方法实现不变性学习的简单方案。其次，我们构建了一组目标函数，使模型能够以无监督方式将医学图像分解为解剖特征集合。最后，我们从实验和理论两方面证明了MONA在三个不同标注设置下的基准数据集上的有效性，在各类半监督设置中均取得了当前最优结果。