The variation in histologic staining between different medical centers is one of the most profound challenges in the field of computer-aided diagnosis. The appearance disparity of pathological whole slide images causes algorithms to become less reliable, which in turn impedes the wide-spread applicability of downstream tasks like cancer diagnosis. Furthermore, different stainings lead to biases in the training which in case of domain shifts negatively affect the test performance. Therefore, in this paper we propose MultiStain-CycleGAN, a multi-domain approach to stain normalization based on CycleGAN. Our modifications to CycleGAN allow us to normalize images of different origins without retraining or using different models. We perform an extensive evaluation of our method using various metrics and compare it to commonly used methods that are multi-domain capable. First, we evaluate how well our method fools a domain classifier that tries to assign a medical center to an image. Then, we test our normalization on the tumor classification performance of a downstream classifier. Furthermore, we evaluate the image quality of the normalized images using the Structural similarity index and the ability to reduce the domain shift using the Fréchet inception distance. We show that our method proves to be multi-domain capable, provides a very high image quality among the compared methods, and can most reliably fool the domain classifier while keeping the tumor classifier performance high. By reducing the domain influence, biases in the data can be removed on the one hand and the origin of the whole slide image can be disguised on the other, thus enhancing patient data privacy.

中文翻译：

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数字病理学的多域染色标准化：整个幻灯片图像的循环一致的对抗网络

不同医疗中心之间组织学染色的差异是计算机辅助诊断领域最深刻的挑战之一。病理全切片图像的外观差异导致算法变得不太可靠，进而阻碍了癌症诊断等下游任务的广泛适用性。此外，不同的染色会导致训练出现偏差，在域转移的情况下会对测试性能产生负面影响。因此，在本文中，我们提出了 MultiStain-CycleGAN，一种基于 CycleGAN 的多域染色归一化方法。我们对 CycleGAN 的修改使我们能够标准化不同来源的图像，而无需重新训练或使用不同的模型。我们使用各种指标对我们的方法进行广泛的评估，并将其与具有多领域能力的常用方法进行比较。首先，我们评估我们的方法如何欺骗试图将医疗中心分配给图像的域分类器。然后，我们测试下游分类器的肿瘤分类性能的标准化。此外，我们使用结构相似性指数评估归一化图像的图像质量，并使用 Fréchet 起始距离减少域偏移的能力。我们表明，我们的方法被证明具有多域能力，在比较方法中提供了非常高的图像质量，并且可以最可靠地欺骗域分类器，同时保持肿瘤分类器的高性能。通过减少域影响，一方面可以消除数据中的偏差，另一方面可以掩盖整个幻灯片图像的来源，从而增强患者数据的隐私性。