Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Martin Halicek, Samuel Ortega, Himar Fabelo, Carlos Lopez, Marylene Lejaune, Gustavo M. Callico, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationDigital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
ISBN (Electronic)9781510634077
StatePublished - 2020
EventMedical Imaging 2020: Digital Pathology - Houston, United States
Duration: Feb 19 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2020: Digital Pathology
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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