Abstract
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 the highest 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.
Abstract (translated)
不同医疗中心的病理学染色差异是计算机辅助诊断领域最深刻的挑战之一。病理整片图像的外观差异导致算法变得不够可靠,这反过来阻碍了像癌症诊断等后续任务的普及应用。此外,不同染色方法会导致训练有偏见,如果域转换负面影响测试性能。因此,在本文中,我们提出了 MultiStain-CycleGAN,一种基于CycleGAN的多域方法,用于染色标准化。我们对CycleGAN进行修改,使其能够让我们不需要重新训练或使用不同模型来标准化来自不同来源的图像。我们使用各种指标进行广泛评估,并比较了常用的多域能力的方法。首先,我们评估我们的方法和试图将医疗中心分配给图像的域分类器的性能如何。然后,我们测试我们的标准化对下游分类器肿瘤分类性能的影响。此外,我们使用结构相似性指数评估标准化图像的质量,并使用弗雷eth inception距离减少域转换的影响。我们表明,我们的方法和相比方法通常使用的多域能力,提供了最高的图像质量,并且最可靠地欺骗域分类器,同时保持肿瘤分类器性能高。通过减少域影响,我们可以消除数据中的偏见,同时隐藏整片图像的来源,从而增强患者数据隐私。
URL
https://arxiv.org/abs/2301.09431
