PRINTER：面向具备原位保真度的虚拟IHC染色的形变感知对抗学习
PRINTER:Deformation-Aware Adversarial Learning for Virtual IHC Staining with In Situ Fidelity
AI总结：
针对连续切片空间错位影响虚拟IHC染色原位保真度的问题，提出融合原型驱动解耦、GapBridge循环配准合成与形变感知对抗学习的弱监督框架PRINTER，在保留H&E细节和染色保真度上优于现有方法，为计算病理虚拟染色提供鲁棒可扩展方案。
摘要：
肿瘤空间异质性分析需要苏木精-伊红（H&E）形态与免疫组化（IHC）生物标志物表达之间存在精确关联，但现有方法存在连续切片空间错位的问题，严重影响原位病理判读的准确性。为获得更精准的虚拟染色结果，我们提出PRINTER——一个弱监督框架，它融合了原型驱动的内容与染色模式解耦，以及形变感知对抗学习策略，旨在准确学习IHC染色模式的同时保留H&E染色细节。该方法包含三项核心创新：（1）原型驱动的染色模式迁移，实现显式的内容-风格解耦；（2）循环配准-合成框架GapBridge，通过可变形结构对齐搭建H&E与IHC域之间的桥梁，配准后的特征引导跨模态风格迁移，而合成输出则迭代优化配准效果；（3）形变感知对抗学习：我们提出一种训练框架，其中生成器与形变感知配准网络共同对聚焦风格的判别器进行对抗优化。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优异，性能优于现有最优方法。本研究为虚拟染色提供了一种鲁棒且可扩展的解决方案，推动了计算病理学领域的发展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
