Stain Transfer

Stain transfer in digital pathology aims to computationally convert images stained with one method (e.g., hematoxylin and eosin) into the appearance of another (e.g., immunohistochemistry), enabling access to information otherwise requiring expensive or unavailable techniques. Current research heavily utilizes generative adversarial networks (GANs), including variations like CycleGANs and StarGAN, to achieve this image-to-image translation, often incorporating region-based guidance for improved accuracy and biological plausibility. This technology offers the potential to improve diagnostic capabilities, expand access to specialized staining, and facilitate the development and training of deep learning models in computational pathology by generating synthetic training data.