标题： PRINTER：具有原位保真度的变形感知对抗学习用于虚拟IHC染色 显示英文标题

标题： PRINTER:Deformation-Aware Adversarial Learning for Virtual IHC Staining with In Situ Fidelity

摘要： 肿瘤空间异质性分析需要精确关联苏木精和伊红（H&E）形态学与免疫组化（IHC）生物标志物表达，但目前的方法在连续切片中存在空间错位，严重损害了原位病理解释。为了获得更准确的虚拟染色模式，我们提出了PRINTER，这是一种弱监督框架，结合了基于原型的内容和染色模式解耦以及变形感知对抗学习策略，旨在准确学习IHC染色模式的同时保留H&E染色细节。我们的方法引入了三个关键创新：(1) 基于原型的染色模式转移，具有显式的内容-风格解耦；(2) 一个循环配准-合成框架GapBridge，通过可变形结构对齐在H&E和IHC领域之间建立桥梁，其中配准特征指导跨模态风格转移，而合成输出迭代优化配准；(3) 变形感知对抗学习：我们提出了一种训练框架，其中生成器和变形感知配准网络共同对抗优化一个以风格为重点的判别器。大量实验表明，PRINTER在保留H&E染色细节和虚拟染色保真度方面表现出色，优于最先进的方法。我们的工作为虚拟染色提供了一个稳健且可扩展的解决方案，推动了计算病理学领域的发展。 显示英文摘要

摘要： Tumor spatial heterogeneity analysis requires precise correlation between Hematoxylin and Eosin H&E morphology and immunohistochemical (IHC) biomarker expression, yet current methods suffer from spatial misalignment in consecutive sections, severely compromising in situ pathological interpretation. In order to obtain a more accurate virtual staining pattern, We propose PRINTER, a weakly-supervised framework that integrates PRototype-drIven content and staiNing patTERn decoupling and deformation-aware adversarial learning strategies designed to accurately learn IHC staining patterns while preserving H&E staining details. Our approach introduces three key innovations: (1) A prototype-driven staining pattern transfer with explicit content-style decoupling; and (2) A cyclic registration-synthesis framework GapBridge that bridges H&E and IHC domains through deformable structural alignment, where registered features guide cross-modal style transfer while synthesized outputs iteratively refine the registration;(3) Deformation-Aware Adversarial Learning: We propose a training framework where a generator and deformation-aware registration network jointly adversarially optimize a style-focused discriminator. Extensive experiments demonstrate that PRINTER effectively achieves superior performance in preserving H&E staining details and virtual staining fidelity, outperforming state-of-the-art methods. Our work provides a robust and scalable solution for virtual staining, advancing the field of computational pathology.