AI RESEARCH
Pretext Matters: An Empirical Study of SSL Methods in Medical Imaging
arXiv CS.CV
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ArXi:2603.22649v1 Announce Type: new Though self-supervised learning (SSL) has nstrated incredible ability to learn robust representations from unlabeled data, the choice of optimal SSL strategy can lead to vastly different performance outcomes in specialized domains. Joint embedding architectures (JEAs) and joint embedding predictive architectures (JEPAs) have shown robustness to noise and strong semantic feature learning compared to pixel reconstruction-based SSL methods, leading to widespread adoption in medical imaging.