Stain Variation

Stain variation in digital pathology images, arising from differences in staining protocols and imaging techniques, significantly hinders the performance of automated image analysis methods. Current research focuses on developing robust algorithms to mitigate this issue, employing techniques such as stain normalization (using single or multiple reference images), stain augmentation (generating synthetically varied images), and adversarial learning (training models to be invariant to stain changes). These advancements aim to improve the accuracy and generalizability of machine learning models for tasks like cell segmentation and tissue classification, ultimately enhancing the efficiency and reliability of digital pathology workflows.