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.
Dem ˇsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (1) (2006) 1–30
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.