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Conferences > Microscopy > 2022 > JM3A > JM3A.25

Monte-Carlo based data generator for Deep Learning applications

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

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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.

© 2022 The Author(s)

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