  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
