  Stain normalization algorithms aim to transform the color and intensity
characteristics of a source multi-gigapixel histology image to match those of a
target image, mitigating inconsistencies in the appearance of stains used to
highlight cellular components in the images. We propose a new approach,
StainFuser, which treats this problem as a style transfer task using a novel
Conditional Latent Diffusion architecture, eliminating the need for handcrafted
color components. With this method, we curate SPI-2M the largest stain
normalization dataset to date of over 2 million histology images with neural
style transfer for high-quality transformations. Trained on this data,
StainFuser outperforms current state-of-the-art deep learning and handcrafted
methods in terms of the quality of normalized images and in terms of downstream
model performance on the CoNIC dataset.
