Why Invariance is Not Enough for Biomedical Domain Generalization and How to Fix It
We present MaskGen, a theoretically grounded and deliberately simple approach for domain generalization in 3D biomedical image segmentation. Modern segmentation models degrade sharply under shifts in modality, disease severity, clinical sites, and more, limiting their reliable adoption. Existing generalization methods address this using extreme augmentations, hand-engineered domain statistics mixing, or architectural redesigns that add significant implementation overhead while yielding inconsistent performance across biomedical settings. MaskGen instead presents a principled learning strategy with marginal overhead that utilizes both source-domain image intensities and domain-stable found...
| Authors | Sebo Diaz, Polina Golland, Elfar Adalsteinsson et al. |
|---|---|
| Primary category | eess.IV |
| Published | 2 April 2026 |
| Last updated at source | 9 May 2026 |
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