Dr. Yujia Xue Profile

Dr. Yujia Xue

at Boston Univ

SPIE Involvement:

Author

Publications (13)

SPIE Journal Paper | 8 May 2023 Open Access

Pupil engineering for extended depth-of-field imaging in a fluorescence miniscope

Joseph Greene, Yujia Xue, Jeffrey Alido, Alex Matlock, Guorong Hu, Kivilcim Kiliç, Ian Davison, Lei Tian

NPh, Vol. 10, Issue 04, 044302, (May 2023) https://doi.org/10.1117/12.10.1117/1.NPh.10.4.044302

KEYWORDS: Diffractive optical elements, GRIN lenses, Tunable filters, Scattering, Point spread functions, Fluorescence imaging, Fluorescence, Depth of field, Engineering, Brain

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Proceedings Article | 16 March 2023 Presentation + Paper

EDoF-Miniscope: a computational miniscope for extended depth-of-field neural imaging

Joseph Greene, Yujia Xue, Jeffrey Alido, Alex Matlock, Guorong Hu, Kivilcim Kiliç, Ian Davison, Lei Tian

Proceedings Volume 12388, 1238808 (2023) https://doi.org/10.1117/12.2648565

KEYWORDS: Fluorescence imaging, Computational imaging, Evolutionary optimization, Scattering, Diffractive optical elements, Point spread functions, Genetic algorithms, Brain, GRIN lenses, Depth of field, Biological imaging

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Proceedings Article | 4 October 2022 Presentation

CM2Net: a deep learning framework for computational miniature mesoscope (Conference Presentation)

Qianwan Yang, Yujia Xue, Guorong Hu, Kehan Guo, Lei Tian

Proceedings Volume PC12204, PC122040D (2022) https://doi.org/10.1117/12.2632322

KEYWORDS: 3D modeling, Data modeling, Stereoscopy, Point spread functions, Resolution enhancement technologies, Particles, Optical design, Microlens, Luminescence, Imaging systems

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Proceedings Article | 1 August 2021 Presentation

Multiplexed cytometry by deep virtual staining from multi-contrast microscopy

Sipei Fu, Yumi Kim, Weiye Song, Yunzhe Li, Yujia Xue, Ji Yi, Lei Tian, Shiyi Cheng

Proceedings Volume 11804, 118041D (2021) https://doi.org/10.1117/12.2594123

KEYWORDS: Microscopy, Multiplexing, Luminescence, Reflectivity, Pathology, Diffraction, Backscatter

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Proceedings Article | 1 August 2021 Presentation

Interpretable deep learning for imaging through scattering medium

Yunzhe Li, Lei Tian, Shiyi Cheng, Yujia Xue

Proceedings Volume 11804, 118041O (2021) https://doi.org/10.1117/12.2594043

KEYWORDS: Light scattering, Scattering, Diffusers, Speckle pattern, Speckle, Neural networks, Model-based design, Matrices, Inverse scattering problem, Glasses

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Proceedings Article | 5 March 2021 Presentation + Paper

Single-shot compressed ultrafast photography in the ultraviolet spectral range

Yingming Lai, Yujia Xue, Christian-Yves Côté, Xianglei Liu, Antoine Laramée, Nicolas Jaouen, François Légaré, Lei Tian, Jinyang Liang

Proceedings Volume 11654, 116540J (2021) https://doi.org/10.1117/12.2579251

KEYWORDS: Ultraviolet radiation, Ultrafast imaging, Reconstruction algorithms, Photography, Ultraviolet photography, Imaging systems, Computer programming, Visible radiation, Streak cameras, Quality systems

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Proceedings Article | 5 March 2021 Presentation

Large-scale high-resolution 3D fluorescence imaging with a computational miniature mesoscope

Yujia Xue, Ian Davison, David Boas, Lei Tian

Proceedings Volume 11649, 1164903 (2021) https://doi.org/10.1117/12.2577667

KEYWORDS: Stereoscopy, Luminescence, 3D image processing, Microlens array, Image resolution, Scattering, Optical fibers, Optical components, Neuroscience, Microscopy

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Proceedings Article | 10 March 2020 Presentation

Towards reliable deep learning based phase microscopy (Conference Presentation)

Shiyi Cheng, Lei Tian, Yunzhe Li, Yujia Xue

Proceedings Volume 11250, 1125015 (2020) https://doi.org/10.1117/12.2543061

KEYWORDS: Microscopy, Biomedical optics, Multiplexing, Neural networks, Evolutionary algorithms, Reliability, Convolutional neural networks, Error analysis, Phase imaging, Microscopes

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Proceedings Article | 13 May 2019 Presentation

Learning speckle correlations for imaging through scattering (Conference Presentation)

Yunzhe Li, Yujia Xue, Lei Tian

Proceedings Volume 10990, 1099002 (2019) https://doi.org/10.1117/12.2520081

KEYWORDS: Speckle, Scattering, Light scattering, Speckle imaging, Diffusers, Deep tissue imaging, Convolutional neural networks, Associative arrays, Scattering media, Statistical modeling

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Proceedings Article | 4 March 2019 Presentation

Learning approach to computational microscopy

Lei Tian, Yujia Xue, Yunzhe Li, Shiyi Cheng

Proceedings Volume 10883, 108830G (2019) https://doi.org/10.1117/12.2515580

KEYWORDS: Microscopy, Scattering media

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Emerging deep learning based computational microscopy techniques promise novel imaging capabilities beyond traditional techniques. In this talk, I will discuss two microscopy applications. First, high space-bandwidth product microscopy typically requires a large number of measurements. I will present a novel physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging,1 enabling significant reduction of the required measurements, opening up real-time applications. In this technique, we design asymmetric coded illumination patterns to encode high-resolution phase information across a wide field-of-view. We then develop a matching DL algorithm to provide large-SBP phase estimation. We demonstrate this technique on both static and dynamic biological samples, and show that it can reliably achieve 5x resolution enhancement across 4x FOVs using only five multiplexed measurements. In addition, we develop an uncertainty learning framework to provide predictive assessment to the reliability of the DL prediction. We show that the predicted uncertainty maps can be used as a surrogate to the true error. We validate the robustness of our technique by analyzing the model uncertainty. We quantify the effect of noise, model errors, incomplete training data, and “out-of-distribution” testing data by assessing the data uncertainty. We further demonstrate that the predicted credibility maps allow identifying spatially and temporally rare biological events. Our technique enables scalable DL-augmented large-SBP phase imaging with reliable predictions. Second, I will turn to the pervasive problem of imaging in scattering media. I will discuss a new deep learning- based technique that is highly generalizable and resilient to statistical variations of the scattering media.2 We develop a statistical ‘one-to-all’ deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.

Proceedings Article | 4 March 2019 Presentation

A deep learning approach to high space-bandwidth product phase microscopy with coded illumination (Conference Presentation)

Yujia Xue, Yunzhe Li, Lei Tian

Proceedings Volume 10887, 108870Y (2019) https://doi.org/10.1117/12.2507006

KEYWORDS: Microscopy, Phase imaging, Phase contrast, Real time imaging, Multiplexing, Phase retrieval, Convolutional neural networks, Image processing

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Proceedings Article | 4 March 2019 Presentation

A one-for-all deep learning approach for imaging through diffusers (Conference Presentation)

Yunzhe Li, Yujia Xue, Lei Tian

Proceedings Volume 10886, 108860X (2019) https://doi.org/10.1117/12.2507276

KEYWORDS: Diffusers, Scattering, Speckle, Light scattering, Deep tissue imaging, Error analysis, Convolutional neural networks, Associative arrays, Scattering media, Statistical modeling

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Proceedings Article | 5 August 2015 Paper

Measurement of PSF for the extended depth of field of microscope based on liquid lens

Yufu Qu, Yujia Xue, Shenyu Zhu

Proceedings Volume 9622, 96221D (2015) https://doi.org/10.1117/12.2193528

KEYWORDS: Liquid lenses, Liquids, Microscopes, Point spread functions, Convolution, Quantum dots, Objectives, Fourier transforms, Image quality, Focus stacking

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Showing 5 of 13 publications

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