StainFuser：控制扩散模型以在多十亿像素组织学图像中实现更快的神经风格迁移
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
AI总结：
本研究针对组织学图像染色外观不一致问题，提出基于条件潜在扩散架构的染色归一化方法StainFuser，构建超200万张图像的SPI-2M数据集，在图像质量和下游任务性能上均优于现有方法。
摘要：
染色归一化算法旨在转换源多十亿像素组织学图像的颜色与强度特征，使其与目标图像匹配，从而缓解用于凸显图像中细胞成分的染色外观不一致问题。我们提出了一种新方法StainFuser，该方法采用新型条件潜在扩散架构，将此问题视为风格迁移任务，无需手工设计的颜色分量。借助该方法，我们构建了SPI-2M——这是迄今为止规模最大的染色归一化数据集，包含超200万张经神经风格迁移实现高质量转换的组织学图像。在该数据集上训练后，StainFuser在归一化图像质量以及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方法和手工设计方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
