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

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.

Automated summary

A method utilizing a generative adversarial network to synthesize hyperspectral imaging from standard RGB images of breast cancer tissue has been proposed in this study. The synthesized hyperspectral images achieved high structural similarity and low mean absolute error compared to actual hyperspectral images. Further research is needed to validate the effectiveness of this method on larger datasets.