Where to Look Matters: Rethinking Sub-Volume Sampling in 3D Medical Self-supervised Learning
Paper recorded by Signals 4 on 2026-10-09 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12
Category: cs.CV · 计算机视觉 · first seen 2026-10-12
Abstract
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 value for representation learning. To this end, we first show through controlled crop-quality manipula
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