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Creators/Authors contains: "Jingxi Li"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, 22nd Annual UC Systemwide Bioengineering Symposium)
    An invasive biopsy followed by histological staining is the gold standard for traditional pathological identification of skin cancers, which requires a long processing time and often results in undesired biopsies and scarring. Reflectance confocal microscopy (RCM) can provide biopsy-free in vivo images of skin structure with a cellular-level resolution for skin disease evaluation; however, the RCM images are grayscale, miss nuclear features and have a low correlation with pathology, which requires specialized training for interpretation. Here, we report a deep learning-based method for performing non-invasive virtual skin histology using in vivo, label-free RCM images. 
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    Accepted Manuscript Full Text Available