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:最近在对比学习方面的研究表明,仅利用极少的标签即可在医学图像分割的情况下取得显着性能。现有方法主要集中在实例识别和不变映射上。然而,它们面临三个常见的问题:(1)尾部分布:医学图像数据通常遵循一个隐式的长尾类分布。盲目利用所有像素进行训练可能导致数据不平衡问题,并造成性能恶化;(2)一致性:由于不同解剖特征之间的类内变异,仍不清楚分割模型是否学习了有意义且一致的解剖特征;以及(3)多样性:整个数据集内的切片内相关性受到了明显较少的关注。这促使我们寻求一种原则性方法,以策略性地利用数据集本身来发现不同解剖视图中相似但又不同的样本。在本文中,我们引入了一种名为矿你自己的解剖学(Mine yOur owN Anatomy, MONA)的新型半监督2D医学图像分割框架,并做出三方面贡献。首先,以往的研究认为,模型训练中每个像素的作用都是相等的。但由于缺乏监督信号,仅此还不足以定义有意义的解剖特征。我们提出了两种简单的解决方案,通过使用更强大的数据增强和最近邻来学习不变量。其次,我们构建了一组目标,鼓励模型能够在无监督的情况下将医学图像分解为一组解剖特征。最后,我们在三个基准数据集上进行了实证和理论上的验证,显示了我们的MONA在不同的标记半监督设置下取得了新的最佳性能。