Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73414
Título: Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology
Autores/as: Halicek, Martin
Ortega Sarmiento, Samuel 
Fabelo Gómez, Himar Antonio 
Lopez, Carlos
Lejaune, Marylene
Marrero Callicó, Gustavo Iván 
Fei, Baowei
Coordinadores/as, Directores/as o Editores/as: Tomaszewski, John E.
Ward, Aaron D.
Clasificación UNESCO: 3314 Tecnología médica
Fecha de publicación: 2020
Editor/a: The international society for optics and photonics (SPIE) 
Resumen: Hyperspectral imaging (HSI), which acquires up to hundreds of bands, has been proposed as a promising imaging modality for digitized histology beyond RGB imaging to provide more quantitative information to assist pathologists with disease detection in samples. While digitized RGB histology is quite standardized and easy to acquire, histological HSI often requires custom-made equipment and longer imaging times compared to RGB. In this work, we present a dataset of corresponding RGB digitized histology and histological HSI of breast cancer, and we develop a conditional generative adversarial network (GAN) to artificially synthesize HSI from standard RGB images of normal and cancer cells. The results of the GAN synthesized HSI are promising, showing structural similarity (SSIM) of approximately 80% and mean absolute error (MAE) of 6 to 11%. Further work is needed to establish the ability of generating HSI from RGB images on larger datasets
URI: http://hdl.handle.net/10553/73414
ISBN: 9781510634077
DOI: 10.1117/12.2549994
Fuente: Proceedings SPIE The International Society for Optical Engineering, n. 11320
Colección:Actas de congresos
miniatura
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