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10 March 2020 Towards reliable deep learning based phase microscopy (Conference Presentation)
Yujia Xue, Shiyi Cheng, Yunzhe Li, Lei Tian
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Proceedings Volume 11250, High-Speed Biomedical Imaging and Spectroscopy V; 1125015 (2020) https://doi.org/10.1117/12.2543061
Event: SPIE BiOS, 2020, San Francisco, California, United States
Abstract
We demonstrate a deep-learning(DL)-based computational microscopy for high-throughput phase imaging by taking multiplexed measurements and employing deep neural networks (DNNs) based reconstruction. In particular, we develop a Bayesian convolutional neural network (BNN) to quantify the uncertainties of the DL inference, providing a surrogate estimate of the true prediction errors. The framework is demonstrated on a high-speed computational phase microscopy technique. We show the BNN is able to not only predict high-resolution phase images and but also provide a pixel-wise credibility map that evaluates the imperfections in the datasets and training process。
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Yujia Xue, Shiyi Cheng, Yunzhe Li, and Lei Tian "Towards reliable deep learning based phase microscopy (Conference Presentation)", Proc. SPIE 11250, High-Speed Biomedical Imaging and Spectroscopy V, 1125015 (10 March 2020); https://doi.org/10.1117/12.2543061
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KEYWORDS
Microscopy
Biomedical optics
Convolutional neural networks
Error analysis
Evolutionary algorithms
Microscopes
Multiplexing
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