Neural network forward model and transfer learning calibration from Monte Carlo to diffuse reflectance spectroscopy

Qing Lan; Ryan G. McClarren; Karthik Vishwanath

doi:10.1117/12.2581556

5 March 2021 Neural network forward model and transfer learning calibration from Monte Carlo to diffuse reflectance spectroscopy

Qing Lan, Ryan G. McClarren, Karthik Vishwanath

Author Affiliations +

Proceedings Volume 11639, Optical Tomography and Spectroscopy of Tissue XIV; 116390E (2021) https://doi.org/10.1117/12.2581556
Event: SPIE BiOS, 2021, Online Only

Abstract

We propose to use neural networks to learn and replace Monte Carlo (MC) simulations. Our neural networks are not only convenient to use but also are shown to be extremely time-saving compared to MC simulations with comparable accuracy. Furthermore, we employ the machine learning method called transfer learning to perform calibration between MC simulated diffuse reflectance and that measured by Diffuse Reflectance Spectroscopy (DRS). The transfer learning model is able to predict DRS measured diffuse reflectance spectrum by training the model using a small amount of DRS data.

Conference Presentation

Citation Download Citation

Qing Lan, Ryan G. McClarren, and Karthik Vishwanath "Neural network forward model and transfer learning calibration from Monte Carlo to diffuse reflectance spectroscopy", Proc. SPIE 11639, Optical Tomography and Spectroscopy of Tissue XIV, 116390E (5 March 2021); https://doi.org/10.1117/12.2581556

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