论文标题
数字病理学中的生成对抗网络：趋势和未来潜力的调查
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
论文作者
论文摘要
数字病理领域的图像分析最近越来越受欢迎。高质量的整个幻灯片扫描仪的使用可以快速获取大量图像数据，同时显示广泛的上下文和微观细节。同时，新颖的机器学习算法提高了图像分析方法的性能。在本文中，我们专注于应用于组织学图像数据的一种特别强大的体系结构，称为生成对抗网络（GAN）。除了提高性能外，GAN还启用了以前棘手的该领域的应用程序场景。但是，甘斯可能表现出引入偏见的潜力。在此，我们总结了概括符号的最新最新开发方案，介绍了甘斯的主要应用，并提供了一些有希望的方法及其未来应用的前景。此外，我们确定目前无法获得未来应用程序的方法。
Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail at the same time. Simultaneously, novel machine learning algorithms have boosted the performance of image analysis approaches. In this paper, we focus on a particularly powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data. Besides improving performance, GANs also enable application scenarios in this field, which were previously intractable. However, GANs could exhibit a potential for introducing bias. Hereby, we summarize the recent state-of-the-art developments in a generalizing notation, present the main applications of GANs and give an outlook of some chosen promising approaches and their possible future applications. In addition, we identify currently unavailable methods with potential for future applications.
