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Conferences > Microscopy > 2022 > MS4A > MS4A.2

Deep learning-enabled realistic virtual histology with ultraviolet scattering and photoacoustic remote sensing microscopy

Matthew T. Martell, Nathaniel J.M. Haven, Ewan A. McAlister, Brendon S. Restall, Sveta Silverman, Lashan Peiris, Benjamin A. Adam, Jean Deschenes, Xingyu Li, and Roger J. Zemp

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Abstract

A cycleGAN model is trained to render UV-scattering and photoacoustic remote sensing virtual images in the style of conventional H&E-stained microscopy. Results are concordant with true H&E-stained histology, offering promising diagnostic utility.

© 2022 The Author(s)

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