Data driven modeling of photonic data

Oleg Ryabchykov; Thomas Bocklitz

doi:10.1117/12.2609418

2 March 2022 Data driven modeling of photonic data

Oleg Ryabchykov, Thomas Bocklitz

Author Affiliations +

Proceedings Volume 11973, Advanced Chemical Microscopy for Life Science and Translational Medicine 2022; 1197302 (2022) https://doi.org/10.1117/12.2609418
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Photonic data can be used to characterize the biochemical composition of samples and often in a non-destructive and label-free manner. To utilize these label-free measurements for applications like diagnostics or analytics, data driven modeling is utilized to translate photonic data into higher-level information. In this contribution, two scenarios of data driven modeling will be presented. We will present the translation of nonlinear multi-contrast images into diagnostic information like tissue types, disease types, and histopathological stainings. Additionally, we will demonstrate deep learning as tool for the extraction of the imaginary part of the third-order susceptibility of spectral CARS measurements.

Citation Download Citation

Oleg Ryabchykov and Thomas Bocklitz "Data driven modeling of photonic data", Proc. SPIE 11973, Advanced Chemical Microscopy for Life Science and Translational Medicine 2022, 1197302 (2 March 2022); https://doi.org/10.1117/12.2609418

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