We report label-free, in vivo virtual histology of skin using reflectance confocal microscopy (RCM). We trained a deep neural network to transform in vivo RCM images of unstained skin into virtually stained H&E-like microscopic images with nuclear contrast. This framework successfully generalized to diverse skin conditions, e.g., normal skin, basal cell carcinoma, and melanocytic nevi, as well as distinct skin layers, including the epidermis, dermal-epidermal junction, and superficial dermis layers. This label-free in vivo skin virtual histology framework can be transformative for faster and more accurate diagnosis of malignant skin neoplasms, with the potential to significantly reduce unnecessary skin biopsies.
Reflectance confocal microscopy (RCM) can provide in vivo images of the skin with cellular-level resolution; however, RCM images are grayscale, lack nuclear features and have a low correlation with histology. We present a deep learning-based virtual staining method to perform non-invasive virtual histology of the skin based on in vivo, label-free RCM images. This virtual histology framework revealed successful inference for various skin conditions, such as basal cell carcinoma, also covering distinct skin layers, including epidermis and dermal-epidermal junction. This method can pave the way for faster and more accurate diagnosis of malignant skin neoplasms while reducing unnecessary biopsies.
Immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) is routinely performed on breast cancer cases to guide immunotherapies and help predict the prognosis of breast tumors. We present a label-free virtual HER2 staining method enabled by deep learning as an alternative digital staining method. Our blinded, quantitative analysis based on three board-certified breast pathologists revealed that evaluating HER2 scores based on virtually-stained HER2 whole slide images (WSIs) is as accurate as standard IHC-stained WSIs. This virtual HER2 staining can be extended to other IHC biomarkers to significantly improve disease diagnostics and prognostics.
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