Towards scalable and reliable deep learning based phase microscopy using intensity-only measurements (Conference Presentation)

Lei Tian

doi:10.1117/12.2548615

11 March 2020 Towards scalable and reliable deep learning based phase microscopy using intensity-only measurements (Conference Presentation)

Lei Tian

Author Affiliations +

Proceedings Volume 11249, Quantitative Phase Imaging VI; 1124912 (2020) https://doi.org/10.1117/12.2548615
Event: SPIE BiOS, 2020, San Francisco, California, United States

Abstract

I will discuss our recent efforts in building deep learning based phase imaging techniques that provide improved scalability and reliability. I will demonstrate a physics guided deep learning imaging approach that enables designing highly efficient multiplexed data acquisition schemes and fully leverages the powerful deep learning-based inverse problem framework. We apply this approach to large space-bandwidth product phase microscopy and intensity diffraction tomography, all implemented on a simple LED-array based computational microscopy platform. I will discuss an uncertainty quantification framework to assess the reliability of the deep learning predictions. Quantifying the uncertainty provides per-pixel evaluation of the prediction’s confidence level as well as the quality of the model and dataset.

Conference Presentation

Citation Download Citation

Lei Tian "Towards scalable and reliable deep learning based phase microscopy using intensity-only measurements (Conference Presentation)", Proc. SPIE 11249, Quantitative Phase Imaging VI, 1124912 (11 March 2020); https://doi.org/10.1117/12.2548615

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