FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy.
A nonlinear map- ping approach to stain normalization in digital histopathology images using image-specific color deconvolution,
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FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology
FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy.