StainFuser: Controlling Diffusion for Faster Neural Style Transfer in
  Multi-Gigapixel Histology Images
Robert Jewsbury Ruoyu Wang Abhir Bhalerao Nasir Rajpoot Quoc Dang Vu
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 GAN and handcrafted methods in
terms of the quality of normalized images. Additionally, compared to existing
approaches, it improves the performance of nuclei instance segmentation and
classification models when used as a test time augmentation method on the
challenging CoNIC dataset. Finally, we apply StainFuser on multi-gigapixel
Whole Slide Images (WSIs) and demonstrate improved performance in terms of
computational efficiency, image quality and consistency across tiles over
current methods.