The adoption of artificial intelligence and digital pathology shows immense promise for transforming healthcare through enhanced efficiency, cost-effectiveness, and patient outcomes. However, real-world clinical deployment of deep learning systems faces major obstacles, including the significant staining variability inherent to histopathology workflows. Differences in protocols, reagents, and scanners cause considerable distribution shifts that undermine model generalization.
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