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20 August 2020 Virtual staining of unlabeled quantitative phase images using deep learning
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Abstract
We demonstrate a deep learning-based technique which digitally stains label-free tissue sections imaged by a holographic microscope. Our trained deep neural network can use quantitative phase microscopy images to generate images equivalent to the same field of view of the specimen, once stained and imaged by a brightfield microscope. We prove the efficacy of this technique by implementing it with different tissue-stain combinations involving human skin, kidney, and liver tissue, stained with Hematoxylin and Eosin, Jones’ stain, and Masson’s trichrome stain, respectively, generating images with equivalent quality to the brightfield microscopy images of the histochemically stained corresponding specimen.
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Yair Rivenson, Tairan Liu, Zhensong Wei, Kevin de Haan, Yibo Zhang, and Aydogan Ozcan "Virtual staining of unlabeled quantitative phase images using deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691J (20 August 2020); https://doi.org/10.1117/12.2567503
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