# Fisher-Guided Knowledge Retention

Clinical images arrive in batches by institution, modality and annotated structure, so a segmentation model must absorb a new imaging distribution and new lesion classes without forgetting what it already learned. FGCARL puts bidirectional feature-statistics alignment and Fisher-weighted parameter constraints into one incremental framework that needs no replay of historical patient data. Moving from UCSF-BMSR to BraTS-METS 2023, where domain and classes shift together, FGCARL with Swin UNETR reaches 68.28% mean Dice across the three tumor regions of the new domain, against 60.94% for the strongest comparator, CLAMTS.

Clinical images arrive in batches by institution, modality and annotated structure, so a segmentation model must absorb a new imaging distribution and new lesion classes without forgetting what it already learned. FGCARL puts bidirectional feature-statistics alignment and Fisher-weighted parameter constraints into one incremental framework that needs no replay of historical patient data. Moving from UCSF-BMSR to BraTS-METS 2023, where domain and classes shift together, FGCARL with Swin UNETR reaches 68.28% mean Dice across the three tumor regions of the new domain, against 60.94% for the strongest comparator, CLAMTS.