标题： 一种用于大规模组织学成像的价值映射虚拟染色框架 显示英文标题

标题： A Value Mapping Virtual Staining Framework for Large-scale Histological Imaging

摘要： 虚拟染色技术的出现为组织病理学领域的研究人员提供了一种快速高效的替代方法。 它使研究人员能够利用未标记的显微样本生成化学染色组织切片的虚拟副本，或促进一种染色类型向另一种染色类型的转换。 生成网络（如CycleGAN）表现出色，为虚拟染色提供了一种无监督学习方法，克服了监督学习中对高质量配对数据的依赖限制。 然而，大规模的颜色转换需要处理大视场图像的补丁，通常会导致显著的边界不一致和伪影。 此外，不同着色模态之间的转换通常需要进一步努力来修改损失函数并调整超参数以进行独立的网络训练。 在本研究中，我们引入了一个适用于各种条件的通用虚拟染色框架。 我们提出了一种基于值映射约束的损失函数，以确保不同病理模态之间虚拟染色的准确性，称为值映射生成对抗网络（VM-GAN）。 同时，我们提出了一种基于置信度的拼接方法，以解决由于分块处理而产生的边界不一致问题。 在具有不同染色协议的多样化数据上的实验结果表明，我们的方法在定量指标和视觉感知方面均取得了更好的效果。 显示英文摘要

摘要： The emergence of virtual staining technology provides a rapid and efficient alternative for researchers in tissue pathology. It enables the utilization of unlabeled microscopic samples to generate virtual replicas of chemically stained histological slices, or facilitate the transformation of one staining type into another. The remarkable performance of generative networks, such as CycleGAN, offers an unsupervised learning approach for virtual coloring, overcoming the limitations of high-quality paired data required in supervised learning. Nevertheless, large-scale color transformation necessitates processing large field-of-view images in patches, often resulting in significant boundary inconsistency and artifacts. Additionally, the transformation between different colorized modalities typically needs further efforts to modify loss functions and tune hyperparameters for independent training of networks. In this study, we introduce a general virtual staining framework that is adaptable to various conditions. We propose a loss function based on the value mapping constraint to ensure the accuracy of virtual coloring between different pathological modalities, termed the Value Mapping Generative Adversarial Network (VM-GAN). Meanwhile, we present a confidence-based tiling method to address the challenge of boundary inconsistency arising from patch-wise processing. Experimental results on diverse data with varying staining protocols demonstrate that our method achieves superior quantitative indicators and improved visual perception.