Generating whole brain diffuse optical tomography data sets for machine learning data augmentation

Zephaniah Phillips V; Seung-Hyun Lee; Eunjeong Choi; Dong Cheon Kim; Ah Song Jang; Hee Kyong Kim; Beop-Min Kim

doi:10.1117/12.2648210

17 March 2023 Generating whole brain diffuse optical tomography data sets for machine learning data augmentation

Zephaniah Phillips V, Seung-Hyun Lee, Eunjeong Choi, Dong Cheon Kim, Ah Song Jang, Hee Kyong Kim, Beop-Min Kim

Proceedings Volume 12364, Clinical and Translational Neurophotonics 2023; 123640C (2023) https://doi.org/10.1117/12.2648210
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Machine learning algorithms require a large and diverse data set for robust training. However, gathering a sufficient number is a difficult task due to time and budget constraints. Generated data sets can augment training data and provide diverse example for training. We propose a method to generate realistic diffuse optical tomography (DOT) data sets based on known physiological components of the DOT signal. We generate three dimensional models of each signal component and seed the hemodynamic response to activate targeted cortices. Our method reduces the need for a large recruitment process and increases the accuracy of machine learning algorithms.

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Zephaniah Phillips V, Seung-Hyun Lee, Eunjeong Choi, Dong Cheon Kim, Ah Song Jang, Hee Kyong Kim, and Beop-Min Kim "Generating whole brain diffuse optical tomography data sets for machine learning data augmentation", Proc. SPIE 12364, Clinical and Translational Neurophotonics 2023, 123640C (17 March 2023); https://doi.org/10.1117/12.2648210

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KEYWORDS

Machine learning

Brain

Diffuse optical tomography

3D modeling

Hemodynamics

Interference (communication)

3D acquisition

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