GCC-GAN and GSN-GAN script and dataset

Published: 11 July 2022| Version 1 | DOI: 10.17632/32bvfw6xhj.1
Massimo Salvi,
Francesco Branciforti,
Kristen Meiburger


This repository contains the scripts used to develop the GCC-GAN and GSN-GAN models for color normalization in the work: - Salvi M., Branciforti F., and Meiburger K. M. , "A novel paradigm for color normalization: a GAN-based pix2pix solution for digital pathology and dermatology", Scientific Reports 2022 Abstract: Color variability represents one of the greatest challenges in color medical imaging. Normalization algorithms have proven to be powerful tools for reducing color variability and standardizing image appearance. Several studies have demonstrated the advantages of using standardized images, both for clinical specialists and for image analysis algorithms. In this paper, we present a new deep learning paradigm for color standardization in two of the most popular color imaging techniques: anatomic pathology and dermatology. We present a color-to-color translation based on generative adversarial networks (GANs) that can standardize images of digital pathology (stain normalization) and dermatology (color constancy). Specifically, we formulate the color normalization task as an image-to-image translation problem, maintaining a pixel-to-pixel correspondence between the original and normalized images. Our approach outperforms existing models in both digital pathology and dermatology. Extensive validation was conducted on public datasets to evaluate the effectiveness of color normalization results on completely external test sets, and our framework proved able to generalize well on unseen data. Finally, we publicly release the source code of our models and the datasets used in the development of our method, to encourage open science.



Image Analysis (Medical Imaging), Deep Learning