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Proceedings Paper

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

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200U (16 March 2020); doi: 10.1117/12.2549994
Show Author Affiliations
Martin Halicek, Univ. of Texas at Dallas (United States)
Georgia Institute of Technology (United States)
Emory Univ. (United States)
Samuel Ortega, Univ. of Texas at Dallas (United States)
Univ. de Las Palmas de Gran Canaria (Spain)
Himar Fabelo, Univ. de Las Palmas de Gran Canaria (Spain)
Carlos Lopez, Hospital de Tortosa Verge de la Cinta (Spain)
Univ. Rovira i Virgili (Spain)
Marylene Lejeune, Hospital de Tortosa Verge de la Cinta (Spain)
Univ. Rovira i Virgili (Spain)
Gustavo M. Callico, Univ. de Las Palmas de Gran Canaria (Spain)
Baowei Fei, The Univ. of Texas at Dallas (United States)
Univ. of Texas Southwestern Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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