@inproceedings{d2cd104bc7c840639c6ae797b7171293,
title = "Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology",
abstract = "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.",
author = "Martin Halicek and Samuel Ortega and Himar Fabelo and Carlos Lopez and Marylene Lejaune and Callico, {Gustavo M.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE. All rights reserved.; Medical Imaging 2020: Digital Pathology ; Conference date: 19-02-2020 Through 20-02-2020",
year = "2020",
doi = "10.1117/12.2549994",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, {John E.} and Ward, {Aaron D.}",
booktitle = "Medical Imaging 2020",
}