Stain Normalization

Stain normalization in digital pathology aims to reduce color variations in histological images caused by differences in staining protocols and scanning equipment, improving the consistency and generalizability of image analysis algorithms. Current research focuses on developing robust and efficient normalization methods, employing various techniques including generative adversarial networks (GANs), U-Net architectures, and self-supervised learning approaches, often integrated with deep learning models for downstream tasks like segmentation and classification. Successful stain normalization is crucial for improving the accuracy and reliability of computer-aided diagnosis systems in pathology, enabling more consistent and objective analysis across different laboratories and datasets.