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Proceedings Paper

Machine learning estimations of tissue optical properties for a multi-layered model (Conference Presentation)

Paper Abstract

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.

Paper Details

Date Published: 9 March 2020
Proc. SPIE 11238, Optical Interactions with Tissue and Cells XXXI, 112380Q (9 March 2020); doi: 10.1117/12.2546816
Show Author Affiliations
Joel N. Bixler, Air Force Research Lab. (United States)
Brett Hokr, Radiance Technologies, Inc. (United States)

Published in SPIE Proceedings Vol. 11238:
Optical Interactions with Tissue and Cells XXXI
Bennett L. Ibey; Norbert Linz, Editor(s)

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