Towards physics-informed, reliable, and interpretable deep learning for scientific imaging

Lei Tian

doi:10.1117/12.2569021

26 August 2020 Towards physics-informed, reliable, and interpretable deep learning for scientific imaging

Lei Tian

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690G (2020) https://doi.org/10.1117/12.2569021
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

Emerging deep learning based computational imaging techniques promise unprecedented imaging capabilities. In this talk, I will discuss our efforts in building such techniques with improved reliability and explainability. First, I will discuss a physics-guided deep learning imaging approach that enables designing highly efficient multiplexed data acquisition schemes for intensity diffraction tomography. I will highlight an uncertainty quantification framework to assess the reliability of the deep learning predictions. Second, I will present a deep learning approach to invert the effects of scattering. The trained network is able to make high-quality object predictions from speckles captured from diffusers entirely different from the training data and is highly robust to defocus across several depths of field. I will highlight a dimensionality reduction approach to explain the underlying statistics learned by the network and understand the capability and limitations for generalization.

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Lei Tian "Towards physics-informed, reliable, and interpretable deep learning for scientific imaging", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690G (26 August 2020); https://doi.org/10.1117/12.2569021

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KEYWORDS

Computational imaging

Reliability

Data acquisition

Data modeling

Diffraction

Diffusers

Inverse problems

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