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13 March 2024 Virtual staining of label-free tissue using deep learning
Aydogan Ozcan
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Proceedings Volume PC12852, Quantitative Phase Imaging X; PC128520W (2024) https://doi.org/10.1117/12.3000656
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Deep learning techniques create new opportunities to revolutionize tissue staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, accurate and environmentally friendly alternatives to standard chemical staining methods. These deep learning-based virtual staining techniques can successfully generate different types of histological stains, including immunohistochemical stains, from label-free microscopic images of unstained samples by using, e.g., autofluorescence microscopy, quantitative phase imaging (QPI) and reflectance confocal microscopy. Similar approaches were also demonstrated for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this presentation, I will provide an overview of our recent work on the use of deep neural networks for label-free tissue staining, also covering their biomedical applications.
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Aydogan Ozcan "Virtual staining of label-free tissue using deep learning", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520W (13 March 2024); https://doi.org/10.1117/12.3000656
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KEYWORDS
Nervous system
Deep learning
Biological samples
Confocal microscopy
Neural networks
Autofluorescence
Biomedical applications
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