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Here, we present a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. Initial reports of this model were used to extract optical properties from a single layer tissue model with high accuracy. Here, we expand this method to demonstrate its ability to extract optical properties from individual layers in a multi-layer model. We demonstrate the accuracy of the method across a very wide parameter space and demonstrate that the method is insensitive to parameter selection of the neural network model itself.
Joel N. Bixler andBrett Hokr
"Machine learning estimations of tissue optical properties for a multi-layered model (Conference Presentation)", Proc. SPIE 11238, Optical Interactions with Tissue and Cells XXXI, 112380Q (9 March 2020); https://doi.org/10.1117/12.2546816
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Joel N. Bixler, Brett Hokr, "Machine learning estimations of tissue optical properties for a multi-layered model (Conference Presentation)," Proc. SPIE 11238, Optical Interactions with Tissue and Cells XXXI, 112380Q (9 March 2020); https://doi.org/10.1117/12.2546816