DermoCC-GAN dataset

This repository contains the image dataset and the scripts used to develop the DermoCC-GAN for standardizing dermatological images: - Salvi M., Branciforti F., Veronese F., Zavattaro E., Tarantino V., Savoia P., and Meiburger K. M. , "DermoCC-GAN: A new approach for standardizing dermatological images using generative adversarial networks", Computer Methods and Programs in Biomedicine 2022 Abstract: Dermatological images are typically diagnosed based on visual analysis of the skin lesion acquired using a dermoscope. However, the final quality of the acquired image is highly dependent on the illumination conditions during the acquisition phase. This variability in the light source can affect the dermatologist's diagnosis and decrease the accuracy of computer-aided diagnosis systems. Color constancy algorithms have proven to be a powerful tool to address this issue by allowing the standardization of the image illumination source, but the most commonly used algorithms still present some inherent limitations due to assumptions made on the original image. In this work, we propose a novel Dermatological Color Constancy Generative Adversarial Network (DermoCC-GAN) algorithm to overcome the current limitations by formulating the color constancy task as an image-to-image translation problem. By training the generative adversarial network with a custom heuristic algorithm that performs well on the training set, the model learns the domain transfer task (from original to standardized image) and is then able to accurately apply the color constancy on test images characterized by different illumination conditions. The proposed algorithm outperforms state-of-the-art color constancy algorithms for dermatological images in terms of normalized median intensity and when using the color-normalized images in a deep learning framework for lesion classification (accuracy of the seven-class classifier: 79.2%) and segmentation (dice score: 90.9%). In addition, we validated the proposed approach on two different external datasets with highly satisfactory results. The novel strategy presented here shows how it is possible to generalize a heuristic method for color constancy for dermatological image analysis by training a GAN. The overall approach presented here can be easily extended to numerous other applications.本仓库包含用于开发DermoCC-GAN以实现皮肤镜图像标准化的图像数据集与配套脚本。 - Salvi M.、Branciforti F.、Veronese F.、Zavattaro E.、Tarantino V.、Savoia P. 与 Meiburger K. M.，《DermoCC-GAN: A new approach for standardizing dermatological images using generative adversarial networks》，《Computer Methods and Programs in Biomedicine》，2022年 摘要： 皮肤镜图像通常基于皮肤镜采集的皮肤皮损视觉分析开展诊断。然而，采集图像的最终质量高度依赖于采集阶段的光照条件。光源的此类差异不仅会干扰皮肤科医生的诊断决策，还会降低计算机辅助诊断系统的识别精度。颜色恒常性算法可实现图像光照源的标准化，已被证明是解决该问题的有力工具，但当前主流的颜色恒常性算法仍因对原始图像的预设假设，存在固有局限性。 在本研究中，我们提出了一种新型皮肤镜颜色恒常性生成对抗网络（DermoCC-GAN, Dermatological Color Constancy Generative Adversarial Network）算法，通过将颜色恒常性任务建模为图像到图像的转换问题，以克服现有局限性。通过使用在训练集上表现优异的自定义启发式算法训练生成对抗网络，模型可学习从原始图像到标准化图像的域迁移任务，进而能够在不同光照条件下的测试图像上精准应用颜色恒常性处理。 所提算法在皮肤镜图像的颜色恒常性任务中，在归一化中位强度指标上优于当前主流算法；且在将颜色归一化图像用于皮损分类的深度学习框架中，七分类器的准确率达79.2%，在分割任务中的骰子系数（Dice Score）达90.9%。此外，我们在两个不同的外部数据集上验证了所提方法，结果均令人十分满意。 本文提出的新型策略证明，通过训练生成对抗网络，可将适用于皮肤镜图像分析的颜色恒常性启发式方法进行泛化。本文所提出的整体方案可轻松扩展至众多其他应用场景。