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Recent studies on contrastive learning have achieved remarkable performancesolely 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 datausually follows an implicit long-tail class distribution. Blindly leveragingall pixels in training hence can lead to the data imbalance issues, and causedeteriorated performance; (2) consistency: it remains unclear whether asegmentation model has learned meaningful and yet consistent anatomicalfeatures due to the intra-class variations between different anatomicalfeatures; and (3) diversity: the intra-slice correlations within the entiredataset have received significantly less attention. This motivates us to seek aprincipled approach for strategically making use of the dataset itself todiscover similar yet distinct samples from different anatomical views. In thispaper, we introduce a novel semi-supervised 2D medical image segmentationframework termed Mine yOur owN Anatomy (MONA), and make three contributions.First, prior work argues that every pixel equally matters to the modeltraining; we observe empirically that this alone is unlikely to definemeaningful anatomical features, mainly due to lacking the supervision signal.We show two simple solutions towards learning invariances - through the use ofstronger data augmentations and nearest neighbors. Second, we construct a setof objectives that encourage the model to be capable of decomposing medicalimages into a collection of anatomical features in an unsupervised manner.Lastly, we both empirically and theoretically, demonstrate the efficacy of ourMONA on three benchmark datasets with different labeled settings, achieving newstate-of-the-art under different labeled semi-supervised settings

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels