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Abstract Introduction Hematoxylin and eosin (HE) is the standard stain used in histology to make tissue visible to the human eye by highlighting certain cellular and tissue structures, and it is a technique that is widely used in the diagnosis of cancer and other pathologies. Chemical staining, however, is irreversible, making the tissue unusable for subsequent measurements, such as spatial transcriptomics. Here we utilize generative AI method based on pix2pix image-to-image translation to generate virtual HE-staining for whole slide images (WSIs) acquired of unstained tissue with brightfield microscopy and perform a thorough histological evaluation for the feasibility in breast cancer diagnostics. Materials and Methods We optimized sample preparation and imaging setup for virtual staining purposes, and developed a custom generative adversarial network architecture for learning the virtual staining from paires samples of unstained and H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 6185.
Latonen et al. (Fri,) studied this question.