Immunohistochemistry Image

Immunohistochemistry (IHC) image analysis focuses on extracting diagnostic information from stained tissue samples, primarily to improve cancer diagnosis and treatment planning. Current research heavily utilizes deep learning, employing architectures like GANs, diffusion models, and transformers, to perform tasks such as virtual IHC staining (generating IHC images from H&E images), automated IHC scoring, and improved segmentation of cells and tissue regions. These advancements aim to reduce the cost and time associated with traditional IHC methods, improve diagnostic accuracy, and enable more objective and reproducible assessments, ultimately impacting both research and clinical practice.