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5 March 2021 Multiplexed virtual fluorescence labeling from multi-contrast microscopy by deep learning
Shiyi Cheng, Sipei Fu, Yumi Mun Kim, Ji Yi, Lei Tian
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Proceedings Volume 11654, High-Speed Biomedical Imaging and Spectroscopy VI; 1165414 (2021) https://doi.org/10.1117/12.2578439
Event: SPIE BiOS, 2021, Online Only
Abstract
Standard immunofluorescence (IF) staining is labor-intensive, time-consuming and suffers from inflexibility and poor multiplicity. To overcome these limitations, we proposed a deep learning (DL) approach for virtual IF staining with high multiplicity and specificity from label-free reflectance microscopy. Our results show that DL-enabled label-free IF microscopy can predict characteristic subcellular features during different cell cycles and reveal cellular phenotypes with high accuracy.
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Shiyi Cheng, Sipei Fu, Yumi Mun Kim, Ji Yi, and Lei Tian "Multiplexed virtual fluorescence labeling from multi-contrast microscopy by deep learning", Proc. SPIE 11654, High-Speed Biomedical Imaging and Spectroscopy VI, 1165414 (5 March 2021); https://doi.org/10.1117/12.2578439
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KEYWORDS
Microscopy
Multiplexing
Reflectivity
Luminescence
Transmittance
Backscatter
Biology
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