Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models

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 profile...

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Detalles Bibliográficos
Autores principales: Carrillo-Perez, Francisco, Pizurica, Marija, Ozawa, Michael G., Vogel, Hannes, West, Robert B., Kong, Christina S., Herrera, Luis Javier, Shen, Jeanne, Gevaert, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475789/
https://www.ncbi.nlm.nih.gov/pubmed/37671024
http://dx.doi.org/10.1016/j.crmeth.2023.100534

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475789/
https://www.ncbi.nlm.nih.gov/pubmed/37671024
http://dx.doi.org/10.1016/j.crmeth.2023.100534

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