  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.
