Formalizes continual segmentation under coupled class-domain-label shifts and introduces gradient-adaptive stabilization plus prototype consistency for semi-supervised learning in heterogeneous dense prediction.
URL https:// repo-prod.prod.sagebase.org/repo/v1/ doi/locate?id=syn3193805&type=ENTITY
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.
citing papers explorer
-
Continual Segmentation under Joint Nonstationarity
Formalizes continual segmentation under coupled class-domain-label shifts and introduces gradient-adaptive stabilization plus prototype consistency for semi-supervised learning in heterogeneous dense prediction.
-
Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Semantic-aware random convolution and intensity-based source matching enable effective single-source domain generalization for medical image segmentation, outperforming prior methods and sometimes matching in-domain performance.