标题： GANs 与扩散模型在 HER2match 数据集虚拟染色中的比较 显示英文标题

标题： GANs vs. Diffusion Models for virtual staining with the HER2match dataset

摘要： 虚拟染色是一种有前景的技术，它使用深度生成模型来重现组织学染色，为传统的组织化学染色提供更快、更经济的替代方案。 特别是对于H&E-HER2染色转移，尽管相关出版物呈上升趋势，但缺乏足够的公共数据集阻碍了该领域的进展。 此外，目前尚不清楚哪种模型框架在此特定任务中表现最佳。 在本文中，我们介绍了HER2match数据集，这是第一个公开可用的数据集，其中包含用H&E和HER2染色的相同乳腺癌组织切片。 此外，我们比较了几种生成对抗网络（GANs）和扩散模型（DMs）的性能，并实现了用于H&E-HER2转换的新颖布朗桥扩散模型（BBDM）。 我们的研究结果表明，总体而言，GANs的表现优于DMs，只有BBDM达到了相当的结果。 此外，我们强调了数据对齐的重要性，因为所有在HER2match上训练的模型相比广泛使用的连续切片BCI数据集，产生了显著改进的视觉效果。 这项研究提供了一个新的高质量数据集（[在发表接受后可获得]），提高了模型训练和评估。 此外，我们对框架的比较为该领域的研究人员提供了有价值的指导。 显示英文摘要

摘要： Virtual staining is a promising technique that uses deep generative models to recreate histological stains, providing a faster and more cost-effective alternative to traditional tissue chemical staining. Specifically for H&E-HER2 staining transfer, despite a rising trend in publications, the lack of sufficient public datasets has hindered progress in the topic. Additionally, it is currently unclear which model frameworks perform best for this particular task. In this paper, we introduce the HER2match dataset, the first publicly available dataset with the same breast cancer tissue sections stained with both H&E and HER2. Furthermore, we compare the performance of several Generative Adversarial Networks (GANs) and Diffusion Models (DMs), and implement a novel Brownian Bridge Diffusion Model for H&E-HER2 translation. Our findings indicate that, overall, GANs perform better than DMs, with only the BBDM achieving comparable results. Furthermore, we emphasize the importance of data alignment, as all models trained on HER2match produced vastly improved visuals compared to the widely used consecutive-slide BCI dataset. This research provides a new high-quality dataset ([available upon publication acceptance]), improving both model training and evaluation. In addition, our comparison of frameworks offers valuable guidance for researchers working on the topic.