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.

通过采用基于条件潜在扩散的新型架构，以样式转移任务的方式处理了镜像污染问题，并建立了迄今为止最大的超过 200 万组织学图像数据集 SPI-2M，并使用神经样式转移进行高质量转换，从而在归一化图像的质量方面胜过了现有的 GAN 和手工方法。此外，作为考验时间增强方法应用于具有挑战性的 CoNIC 数据集的核实例分割和分类模型，与现有方法相比，它改进了性能。最后，在多千兆像素全尺寸图像（Whole Slide Images，WSIs）上应用了 StainFuser，并展示了相对于现有方法，它在计算效率、图像质量和图块之间的一致性方面的改进。