Virtual Histological Staining Using Deep Learning

Thomas M. Menino Convention & Exhibition Center 

Virtual staining is a cross-domain image synthesis approach that applies deep learning to generate digitally "stained" pathology images—either from unstained tissue or by translating one staining modality into another (e.g., mIF to H&E). This approach reduces reliance on labor-intensive laboratory protocols and enables scalable, cost-effective digital alternatives. Leveraging co-registered mIF and H&E whole-slide images, we align cell type labels derived from mIF (via the CellGate pipeline) with H&E slides to create a large-scale dataset of labeled H&E images at single-cell resolution. These data provide a powerful resource for training virtual staining models that enable automated, cell-level interpretation of H&E slides without requiring multiplexed imaging. Virtual staining thus expands access to high-quality spatial tumor microenvironment analysis, advancing both research and clinical applications.