Stain Invariant

Stain invariance in computational pathology focuses on developing deep learning models robust to variations in tissue staining, a major source of domain shift hindering the generalization of algorithms across different datasets. Current research emphasizes semi-supervised and self-supervised learning approaches, often employing contrastive learning, cycleGANs, or adversarial training within convolutional neural networks to learn stain-invariant features. These advancements aim to improve the accuracy and reliability of automated histopathological image analysis, ultimately leading to more consistent and efficient diagnostic tools in healthcare.