@misc{10481/85217,
year = {2023},
month = {8},
url = {https://hdl.handle.net/10481/85217},
abstract = {In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative
models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that
learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use
this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex
tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred
by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN
needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to
gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is
available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/,
and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.},
organization = {Grants PID2021-
128317OB-I00},
organization = {MCIN/AEI/10.13039/501100011033},
organization = {Project
P20-00163, funded by Consejerı´a de Universidad, Investigacio´ n e Innovacio},
organization = {ERDF A way of making Europe},
publisher = {Elsevier},
title = {Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models},
doi = {10.1016/j.crmeth.2023.100534},
author = {Carrillo Pérez, Francisco and Herrera Maldonado, Luis Javier},
}