GCC-GAN and GSN-GAN script and dataset

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., Molinari F., and Meiburger K. M. , "Generative models for color normalization in digital pathology and dermatology: advancing the learning paradigm", Expert Systems with Applications 2024, DOI: 10.1016/j.eswa.2023.123105

Abstract:
Color medical images introduce an additional confounding factor when compared to conventional grayscale medical images: color variability. This variability alludes to potential critical issues of inconsistent evaluation by clinicians and the misinterpretation or suboptimal learning process of automatic quantitative algorithms. To mitigate the potential negative ramifications of color variability, various color normalization strategies have been introduced and have proven to be a powerful tool for standardizing image appearance. Here we present a novel paradigm for the color normalization process that is based on generative adversarial networks (GANs) that can standardize images of digital pathology (stain normalization) and dermatology (color constancy), two foundational research fields in which the acquired images consistently present high color variability. 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 state of the art methods in both the digital pathology and dermatology clinical fields. Extensive validation was conducted on public datasets to evaluate the effectiveness of color normalization results on completely external test sets. Our framework generalized well on unseen data, showing how it can be effectively included in the pipeline of automatic quantitative algorithms for reducing color variability and hence enhancing final segmentation and/or classification performance. Finally, we publicly release the source code of our models to encourage open science.

本存储库包含了开发 GCC-GAN 和 GSN-GAN 模型以实现数字病理学和皮肤病学中图像颜色归一化的脚本，相关研究成果发表在 Salvi M. 等人撰写的《Expert Systems with Applications》2024年刊上，DOI：10.1016/j.eswa.2023.123105。

摘要：与传统的灰度医学图像相比，彩色医学图像引入了额外的混杂因素：颜色可变性。这种可变性暗示了临床医生评估的不一致，以及自动定量算法对图像误解或学习过程不优的潜在关键问题。为减轻颜色可变性的潜在负面影响，已引入各种颜色归一化策略，并已被证明是标准化图像外观的强大工具。在此，我们提出了一种基于生成对抗网络（GANs）的彩色归一化过程的新范式，该范式能够标准化数字病理学（染色归一化）和皮肤病学（颜色恒定性）图像，这两个研究领域均以图像颜色可变性高为特征。具体而言，我们将颜色归一化任务表述为一种图像到图像的翻译问题，保持原始图像与归一化图像之间的像素对应关系。我们的方法在数字病理学和皮肤病学临床领域均优于现有的最先进方法。我们在公共数据集上进行了广泛的验证，以评估颜色归一化结果在完全外部测试集上的有效性。我们的框架在未见数据上表现出良好的泛化能力，展示了它如何有效地纳入自动定量算法的流程中，以减少颜色可变性，从而增强最终的分割和/或分类性能。最后，我们公开发布了模型的源代码，以鼓励开放科学。