Conventional histopathology involves expensive and labor intensive processes that often consume tissue samples, rendering them unavailable for other analysis. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to non-destructively image the autofluorescence of unstained tissue sections. We then train a deep learning model to use the autofluorescence to generate virtual histological stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histological features present in the real stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study where both real and virtual stains are scored by pathologists. The test showed moderate to good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH clinical research network (NASH CRN) score prediction. We showed that the end-to-end automated pathology platform is comparable to pathologists for NASH CRN scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology, and enable novel approaches to spatial biology research.

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