Stain Augmentation

Stain augmentation in digital pathology aims to improve the robustness of deep learning models against variations in tissue staining and imaging procedures, a major obstacle to reliable automated diagnosis. Current research focuses on developing novel augmentation techniques, often integrated with stain normalization or multi-task learning frameworks, utilizing architectures like GANs, U-Nets, and style transfer methods to generate realistic synthetic stain variations. These advancements enhance model generalization across different scanners and staining protocols, ultimately leading to more accurate and reliable computational pathology tools for clinical applications.