数字病理学中的多域染色归一化：一种用于全切片图像的循环一致对抗网络
Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images
AI总结：
本文提出 MultiStain-CycleGAN，通过改进 CycleGAN 实现无需重训的多域病理全切片图像染色归一化，并在图像质量、域分类欺骗和肿瘤分类保持上优于现有多域方法。
摘要：
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严峻的挑战之一。病理全切片图像的外观差异会降低算法的可靠性，进而阻碍癌症诊断等下游任务的广泛应用。此外，不同染色会导致训练偏差，在发生域迁移时对测试性能产生负面影响。因此，本文提出 MultiStain-CycleGAN，这是一种基于 CycleGAN 的多域染色归一化方法。我们对 CycleGAN 的修改使其能够在无需重新训练或使用不同模型的情况下，对不同来源的图像进行归一化。我们使用多种指标对该方法进行了广泛评估，并将其与常用的、具备多域处理能力的方法进行比较。首先，我们评估该方法在多大程度上能够欺骗一个试图为图像判定所属医疗中心的域分类器。随后，我们在下游分类器的肿瘤分类性能上测试归一化效果。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 inception distance 评估其降低域迁移的能力。结果表明，我们的方法具备多域处理能力，在所比较的方法中提供最高的图像质量，并且能够在保持肿瘤分类器较高性能的同时，最可靠地欺骗域分类器。通过降低域影响，一方面可以消除数据中的偏差，另一方面可以伪装全切片图像的来源，从而增强患者数据隐私。
英文摘要：
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.
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。
