标题： 基于标记条件扩散模型的组织学图像虚拟多重染色 显示英文标题

标题： Virtual Multiplex Staining for Histological Images using a Marker-wise Conditioned Diffusion Model

摘要： 多路成像正在通过在组织样本中同时可视化多个生物标志物来革新病理学，提供传统苏木精和伊红（H&E）染色无法提供的分子级见解。 然而，多路数据采集的复杂性和成本阻碍了其广泛应用。 此外，大多数现有的H&E图像大型数据库缺乏相应的多路图像，限制了多模态分析的机会。 为解决这些挑战，我们利用了潜在扩散模型（LDMs）的最新进展，这些模型擅长通过其强大的先验来建模复杂的数据分布，以微调到目标领域。 在本文中，我们引入了一种新的虚拟多路染色框架，该框架利用预训练的LDM参数，通过条件扩散模型从H&E图像生成多路图像。 我们的方法通过将扩散模型针对每个标记进行条件设置，实现逐个标记的生成，同时所有标记共享相同的架构。 为了应对不同标记染色之间像素值分布的变化，并提高推理速度，我们对模型进行了单步采样的微调，通过像素级损失函数提高了颜色对比度保真度和推理效率。 我们在两个公开可用的数据集上验证了我们的框架，显著展示了其在生成多达18种不同标记类型方面的有效性，相较于之前方法实现的2-3种标记类型，准确性有了显著提升。 这种验证突显了我们框架的潜力，开创了虚拟多路染色的新局面。 最后，本文弥合了H&E和多路成像之间的差距，可能使回顾性研究和现有H&E图像库的大规模分析成为可能。 显示英文摘要

摘要： Multiplex imaging is revolutionizing pathology by enabling the simultaneous visualization of multiple biomarkers within tissue samples, providing molecular-level insights that traditional hematoxylin and eosin (H&E) staining cannot provide. However, the complexity and cost of multiplex data acquisition have hindered its widespread adoption. Additionally, most existing large repositories of H&E images lack corresponding multiplex images, limiting opportunities for multimodal analysis. To address these challenges, we leverage recent advances in latent diffusion models (LDMs), which excel at modeling complex data distributions utilizing their powerful priors for fine-tuning to a target domain. In this paper, we introduce a novel framework for virtual multiplex staining that utilizes pretrained LDM parameters to generate multiplex images from H&E images using a conditional diffusion model. Our approach enables marker-by-marker generation by conditioning the diffusion model on each marker, while sharing the same architecture across all markers. To tackle the challenge of varying pixel value distributions across different marker stains and to improve inference speed, we fine-tune the model for single-step sampling, enhancing both color contrast fidelity and inference efficiency through pixel-level loss functions. We validate our framework on two publicly available datasets, notably demonstrating its effectiveness in generating up to 18 different marker types with improved accuracy, a substantial increase over the 2-3 marker types achieved in previous approaches. This validation highlights the potential of our framework, pioneering virtual multiplex staining. Finally, this paper bridges the gap between H&E and multiplex imaging, potentially enabling retrospective studies and large-scale analyses of existing H&E image repositories.