Virtual Staining

Virtual staining studies how to generate the appearance of a chemical or molecular stain from another tissue image, such as predicting IHC markers from H&E or stain-like images from label-free microscopy. GANs and diffusion models learn these translations from paired or unpaired images, while newer approaches add pathology foundation-model features, segmentation priors, or biological and spatial constraints. The goal is to make biomarker interpretation and downstream analysis more accessible without performing every physical stain. A central challenge is that realistic-looking images may still misrepresent tissue structure or marker expression, so evaluation increasingly combines perceptual quality with task-specific accuracy, morphology checks, and uncertainty estimates.