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5 March 2021 Deep learning for high-quality imaging and accurate classification of cells through Anderson localizing optical fiber
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Abstract
We demonstrate highly accurate and fast cell imaging and classification systems enabled by the combination of disordered optical fiber for data transport and deep convolutional neural networks (DCNNs) for data analysis. Disordered optical fiber feature unique light transport properties based on the principle of transverse Anderson localization. A dense network of single mode-like transmission channels results in high spatial resolution while providing robustness regarding bending and environmental changes. DCNNs optimized for cell image reconstruction or cell classification have been trained and applied to perform rigorous testing. We show artifact-free real-time image reconstruction and >90% correct classification of cell samples.
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Axel Schülzgen, Jian Zhao, Shengli Fan, Jose Enrique Antonio-Lopez, Rodrigo Amezcua Correa, and Xiaowen Hu "Deep learning for high-quality imaging and accurate classification of cells through Anderson localizing optical fiber", Proc. SPIE 11703, AI and Optical Data Sciences II, 117030Q (5 March 2021); https://doi.org/10.1117/12.2578621
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