提出染色不变特征学习方法，提升病理图像分类在不同染色条件下的泛化能力。
Stain-Invariant Representation for Tissue Classification in Histology Images
- 通过扰动染色矩阵生成训练图像的染色增强版本
- 引入染色正则化损失，使原图与增强图特征一致
- 适用于多中心、多染色协议的病理图像分类任务
组织学切片数字化过程中，染色方案、扫描仪和组织类型等因素会导致全幻灯片图像（WSI）外观差异，形成领域偏移，给多队列环境下深度学习模型的训练与测试带来显著挑战。为此，本文提出一种框架：利用染色矩阵扰动生成训练图像的染色增强版本，并采用染色正则化损失，强制源图像与增强图像的特征表示保持一致，从而促使模型学习染色不变、进而领域不变的特征表示。我们在结直肠癌图像的跨域多类别组织类型分类任务上评估了该方法，性能优于现有先进方法。
原文摘要 · Abstract (English)
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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