Deep learning-assisted holo-tomographic flow cytometry with sparse data

doi:10.1016/j.optlastec.2025.113623

Deep learning-assisted holo-tomographic flow cytometry with sparse data

2025-07-21

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DOI：10.1016/j.optlastec.2025.113623

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Abstract

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• Holo-tomographic flow cytometry reconstruction with sparse data can be achieved. • Use physics-driven neural network to achieve unsupervised learning. • Introduce wave propagation model to accommodate multi-scattering samples. • Has good generalization and robustness.

Keywords:

holo-tomographic flow cytometry

physics-driven neural network

unsupervised learning

wave propagation model

multi-scattering samples

Deep learning-assisted holo-tomographic flow cytometry with sparse data

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