AI RESEARCH
Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
arXiv CS.CV
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ArXi:2604.02564v1 Announce Type: cross We present DropGen, a simple and theoretically-grounded approach for domain generalization in 3D biomedical image segmentation. Modern segmentation models degrade sharply under shifts in modality, disease severity, clinical sites, and other factors, creating brittle models that limit reliable deployment. Existing domain generalization methods rely on extreme augmentations, mixing domain statistics, or architectural redesigns, yet incur significant implementation overhead and yield inconsistent performance across biomedical settings.