用于组织病理学染色归一化的生成对抗网络
Generative Adversarial Networks for Stain Normalisation in Histopathology
AI总结：
针对数字病理图像视觉差异大导致AI模型泛化差的问题，本章梳理了染色归一化技术，重点对比基于GAN与非生成式方法的优劣，指出该领域仍需探索高效可靠的归一化方案以提升AI模型鲁棒性。
摘要：
近年来数字病理学的快速发展为开发基于人工智能的工具以提升临床诊断的准确性和效率提供了理想契机。当前研究面临的重大阻碍之一是数字病理图像间存在高度视觉差异性，导致模型对未见过的数据泛化能力较差。染色归一化旨在在不改变图像结构内容的前提下，统一数字病理图像的视觉特征。本章探讨了已应用于数字病理染色归一化的各类技术，重点关注利用生成对抗网络（GANs）的方法。通常而言，基于GAN的方法表现优于非生成式方法，但代价是计算需求大幅提升。不过目前尚不清楚哪种方法总体上最适合染色归一化——不同的GAN与非GAN方法在不同场景、不同性能指标下各有优劣。这是一个仍在发展的研究领域，研究人员正致力于找到一种高效且有效的病理图像归一化方法，使AI模型更具鲁棒性与泛化能力。
英文摘要：
The rapid growth of digital pathology in recent years has provided an ideal opportunity for the development of artificial intelligence-based tools to improve the accuracy and efficiency of clinical diagnoses. One of the significant roadblocks to current research is the high level of visual variability across digital pathology images, causing models to generalise poorly to unseen data. Stain normalisation aims to standardise the visual profile of digital pathology images without changing the structural content of the images. In this chapter, we explore different techniques which have been used for stain normalisation in digital pathology, with a focus on approaches which utilise generative adversarial networks (GANs). Typically, GAN-based methods outperform non-generative approaches but at the cost of much greater computational requirements. However, it is not clear which method is best for stain normalisation in general, with different GAN and non-GAN approaches outperforming each other in different scenarios and according to different performance metrics. This is an ongoing field of study as researchers aim to identify a method which efficiently and effectively normalises pathology images to make AI models more robust and generalisable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
