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Where to Look Matters: Rethinking Sub-Volume Sampling in 3D Medical Self-supervised Learning
Self-supervised learning on 3D medical images commonly relies on random sub-volume sampling during pretraining. However, random cropping implicitly treats all spatial regions as equally worth observing, despite anatomical priors and spatial redundancy that make regions inherently unequal in their va
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