H&E to IHC virtual staining methods in breast cancer: an overview and benchmarking
Pascal Klöckner, José Alberto Teixeira, Diana Montezuma, João Fraga, Hugo M. Horlings, Jaime S. Cardoso, Sara P. Oliveira
Abstract
Immunohistochemistry (IHC) is crucial for the clinical categorisation of breast cancer cases. Deep generative models may offer a cost-effective alternative by virtually generating IHC images from hematoxylin and eosin samples. This review explores the state-of-the-art in virtual staining for breast cancer biomarkers (HER2, PgR, ER and Ki-67) and benchmarks several models on public datasets. It serves as a resource for researchers and clinicians interested in applying or developing virtual staining techniques.
Topics & Concepts
ImmunohistochemistryBreast cancerH&E stainStainingBenchmarkingPathologyComputer scienceMedicineCancerOncologyInternal medicineBusinessMarketingCell Image Analysis TechniquesAI in cancer detectionMolecular Biology Techniques and Applications