Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels. (arXiv:2209.13476v2 [eess.IV] UPDATED)

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 …

arxiv image labels medical segmentation