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    Mine yOur owN Anatomy: revisiting medical image segmentation with extremely limited labels by You, C, Dai, W, Liu, F, Min, Y, Dvornek, NC, Li, X, Clifton, DA, Staib, L, Duncan, JS

    Published 2024
    “…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. …”
    Journal article