Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation

ArXi:2603.13328v1 Announce Type: cross Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or constraints on the latent space appears to be contingent upon the level and/or depth of supervision during model.