组织学图像中组织分类的染色不变表示
Stain-Invariant Representation for Tissue Classification in Histology Images
AI总结：
针对计算病理学中的染色领域偏移问题，本文提出通过染色矩阵扰动生成增强图像并结合染色正则化损失，学习染色不变特征表示，在结直肠癌跨领域组织分类中取得优于现有方法的性能。
摘要：
组织学切片数字化的过程涉及多种因素，这些因素会影响全切片图像（WSI）的最终外观，包括染色方案、扫描仪和组织类型。这种可变性构成了领域偏移，并在多队列环境中训练和测试深度学习（DL）算法时导致显著问题。因此，在计算病理学（CPath）中开发稳健且可泛化的深度学习模型仍然是一个开放性挑战。为此，我们提出了一种框架，该框架利用染色矩阵扰动生成训练图像的染色增强版本。随后，我们采用染色正则化损失来强制源图像与增强图像的特征表示之间保持一致。这样做促使模型学习染色不变的、进而领域不变的特征表示。我们在结直肠癌图像的跨领域多类别组织类型分类上评估了所提出模型的性能，并与其他最先进方法相比取得了改进的性能。
英文摘要：
The process of digitising histology slides involves multiple factors that can affect a whole slide image's (WSI) final appearance, including the staining protocol, scanner, and tissue type. This variability constitutes a domain shift and results in significant problems when training and testing deep learning (DL) algorithms in multi-cohort settings. As such, developing robust and generalisable DL models in computational pathology (CPath) remains an open challenge. In this regard, we propose a framework that generates stain-augmented versions of the training images using stain matrix perturbation. Thereafter, we employed a stain regularisation loss to enforce consistency between the feature representations of the source and augmented images. Doing so encourages the model to learn stain-invariant and, consequently, domain-invariant feature representations. We evaluate the performance of the proposed model on cross-domain multi-class tissue type classification of colorectal cancer images and have achieved improved performance compared to other state-of-the-art methods.
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