Monte-Carlo based data generator for Deep Learning applications

Marien Ochoa; Jason Smith; Navid Nizam; Xavier Intes

doi:10.1364/TRANSLATIONAL.2022.JM3A.25

Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN)
Technical Digest Series (Optica Publishing Group, 2022),
paper JM3A.25
•https://doi.org/10.1364/TRANSLATIONAL.2022.JM3A.25

Monte-Carlo based data generator for Deep Learning applications

Marien Ochoa, Jason Smith, Navid Nizam, and Xavier Intes

Clinical and Translational Biophotonics 2022

Fort Lauderdale, Florida United States
24–27 April 2022
ISBN: 978-1-957171-03-6

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Joint Poster Session I On Site (JM3A)

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M. Ochoa, J. Smith, N. Nizam, and X. Intes, "Monte-Carlo based data generator for Deep Learning applications," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper JM3A.25.

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Abstract

We report on a Monte Carlo-based data generation pipeline to create efficiently large data sets for Deep Learning model training and validation. The pipeline was validated under three different experimental setups and CNN architectures.

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