发表机构
Xiamen University; Univ Montpellier; IMT Mines Ales; Centre Hospitalier Universitaire de Nîmes; Université de Montpellier（厦门大学; 蒙彼利埃大学; 阿尔比-加兹矿业电信学院; 尼姆大学医院; 蒙彼利埃大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出半监督虚拟染色框架，结合配对与未配对数据，通过形态保留和病理真实性约束实现稳定染色，在多类病理图像转换任务中提升了质量与诊断性能。
AI 中文摘要
虚拟染色旨在通过计算生成目标染色的组织病理图像，同时降低传统染色流程的成本与时间。然而，现有方法主要依赖严格配对且配准准确的训练数据，这类数据在常规实践中难以获取且成本高昂。为减少对该类数据的依赖，我们提出一种稳定的半监督虚拟染色框架，该框架联合利用有限的配对数据与大量未配对的源图像。直接融入未配对图像存在挑战，因为其生成结果缺乏对应的目标用于监督，可能导致染色不真实、形态退化甚至训练崩溃。为从这些图像中获取可靠监督，基于Hessian的形态保留方法从每个源图像中提取结构线索，并约束生成的输出保留组织形态；组织病理真实性约束进一步引导输出朝向合理的目标染色特征，防止源自源图像的结构监督退化为轮廓增强或简单颜色变换。这两个组件共同抑制结构与外观漂移，稳定半监督染色转换，并促进保留诊断相关信息。针对Ki67和HER2的H&E到IHC转换，以及FFPE到H&E转换的大量实验，在图像质量、形态保留、鲁棒性及下游诊断性能上均展示出一致提升，代码将公开。
英文摘要
Virtual staining aims to computationally generate target-stained histopathological images while reducing the cost and time associated with conventional staining procedures. However, existing methods rely predominantly on strictly paired and accurately registered training data, which are difficult and expensive to obtain in routine practice. To reduce this dependence, we propose a stable semi-supervised virtual staining framework that jointly exploits both limited paired data and abundant unpaired source images. Directly incorporating unpaired images is challenging because their generated results lack corresponding targets for supervision, potentially leading to unrealistic staining, morphological degradation, or even training collapse. To obtain reliable supervision from these images, Hessian-derived morphology preservation extracts structural cues from each source image and constrains the generated output to retain tissue morphology. Histopathological realism constraints further guide the output toward plausible target-stain characteristics, preventing the source-derived structural supervision from degenerating into contour enhancement or simple color transformation. Together, the two components suppress structural and appearance drift, stabilize semi-supervised stain translation, and promote the preservation of diagnostically relevant information. Extensive experiments on H&E-to-IHC translation for Ki67 and HER2, as well as FFPE-to-H&E translation, demonstrate consistent improvements in image quality, morphology preservation, robustness, and downstream diagnostic performance. Code will be available.
Comments10 pages
