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.

翻译：染色归一化算法旨在将源多千兆像素组织学图像的色彩与强度特征转换为与目标图像相匹配，从而缓解用于突显图像中细胞成分的染色剂在外观上的不一致性。我们提出了一种新方法StainFuser，该方法将问题视为风格迁移任务，并采用一种新颖的条件隐扩散模型架构，无需手工设计色彩成分。基于此方法，我们构建了迄今为止最大的染色归一化数据集SPI-2M，包含超过200万张通过神经风格迁移实现高质量转换的组织学图像。在此数据上训练的StainFuser，在归一化图像质量方面以及在CoNIC数据集上的下游模型性能方面，均优于当前最先进的深度学习和手工设计方法。