H&E to IHC Stain Translation

H&E-to-IHC stain translation aims to computationally convert readily available hematoxylin and eosin (H&E) stained tissue images into immunohistochemistry (IHC) images, which reveal specific molecular information crucial for diagnosis and treatment. Current research focuses on deep learning approaches, particularly generative adversarial networks (GANs) and image-to-image translation models incorporating attention mechanisms and contrastive learning strategies to improve accuracy and address challenges like multi-magnification processing and inconsistent ground truth data. This technology promises to significantly reduce the cost and time associated with IHC staining, improving access to precise cancer diagnostics and facilitating more efficient research on the tumor microenvironment.