Traditional staining normalization approaches, e.g. Macenko, typically rely
on the choice of a single representative reference image, which may not
adequately account for the diverse staining patterns of datasets collected in
practical scenarios. In this study, we introduce a novel approach that
leverages multiple reference images to enhance robustness against stain
variation. Our method is parameter-free and can be adopted in existing
computational pathology pipelines with no significant changes. We evaluate the
effectiveness of our method through experiments using a deep-learning pipeline
for automatic nuclei segmentation on colorectal images. Our results show that
by leveraging multiple reference images, better results can be achieved when
generalizing to external data, where the staining can widely differ from the
training set.

传统的染色标准化方法通常依赖于选择单个代表性参考图像，而这可能不能充分考虑在实际情况下收集的数据集的各种染色模式。本研究引入了一种新颖的方法，利用多个参考图像增强对染色变异的鲁棒性。我们的方法无需参数，并可以在现有的计算病理学流程中采用而无需重大更改。我们通过在结肠癌图像上使用深度学习流程进行自动核分割的实验来评估我们方法的有效性。结果表明，通过利用多个参考图像，可以在广义到外部数据时获得更好的结果，而这些数据的染色与训练集有很大的差异。