At SITC 2024, Pictor Labs—alongside Leica Biosystems and the University of Maryland—demonstrates how virtual H&E (vHE) staining enables regulatory T-cell subtyping and tumor microenvironment analysis in cancer research.
At AACR 2024, Pictor Labs presents virtual multiplex special stains and IHC to support immune phenotyping and classification of lung carcinoma for pathologists.
At USCAP 2024, Pictor Labs, with Johns Hopkins, UCLA, and University of Maryland, presents a study comparing AI computational H&E staining to chemical staining for lymphoma diagnosis.
Published in Journal of Hematopathology 2024, Pictor Labs’ AI-driven virtual staining technology rapidly generates high-quality digital pathology images and multiplex immunostains from unstained FFPE tissue slides—enhancing diagnostic accuracy, preserving valuable biopsy samples, and accelerating hematopathology research and clinical workflows.
Published in International Journal of Surgical Pathology 2024, Pictor Labs demonstrates AI-based computational H&E staining that enables advanced spatial transcriptomic analysis in classic Hodgkin lymphoma, enhancing lymphoma research with cutting-edge machine learning and neural network technology.
At AACR 2023—in collaboration with NanoString and the University of Maryland School of Medicine—Pictor Labs presents virtual staining–enabled morphological and spatial transcriptomic analysis of malignant B cells and the tumor microenvironment.
At ASCO Breakthrough 2023, Pictor Labs—in collaboration with the University of Maryland—leverages AI-driven virtual staining technology to rapidly generate multiplex virtual IHC markers (CD3, CD20, and PAX5) from a single unstained tissue, accelerating immune cell phenotyping and tumor analysis.
At SITC 2023, Pictor Labs showcases AI-driven virtual staining generating multiplex virtual stains (PanCK, CD45 LCA, H&E) from a single unstained tissue, enhancing PD-L1 scoring and patient selection in non-small cell lung cancer (NSCLC) immunotherapy.
Published in BMEF: A Science Partner Journal 2022, Pictor Labs presents an AI-based method that replicates HER2 immunohistochemistry from autofluorescence breast tissue images, offering a faster, cost-effective alternative for cancer diagnostics.
Published in Nature Communications 2021, Pictor Labs demonstrates AI-based transformation of H&E-stained kidney biopsies into virtual special stains (Masson’s Trichrome, PAS, Jones), enabling improved research workflows and supporting histological assessment of non-neoplastic kidney disease.
In collaboration with Charles River, Pictor Labs' AI-driven virtual staining generates H&E and Fluoro-Jade B images from unstained brain sections, streamlining neuronal degeneration assessment in nonclinical neuorotoxicity studies (kainic acid rat model)
Published in Light: Science & Applications 2020, Pictor Labs' deep learning framework enables virtual multiplex staining of label-free tissue by digitally applying different histological stains—such as H&E, Jones, and Trichrome—to distinct tissue microstructures within the same section, supporting advanced spatial tissue analysis.
Published in Nature Biomedical Engineering 2019, Pictor Labs' deep learning-based virtual staining transforms autofluorescence images into brightfield-equivalent stains, eliminating chemical staining and accelerating tissue analysis. Validated across organs and stain types by board-certified pathologists.
