Abstract
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.
Abstract (translated)
肿瘤空间异质性分析需要精确地将苏木精和伊红（H&E）形态学与免疫组化（IHC）生物标志物表达相关联，然而当前的方法因连续切片中的空间对齐问题而遭受重大影响，这严重削弱了原位病理解释的准确性。为了获得更准确的虚拟染色模式，我们提出了一种弱监督框架——PRINTER，该框架集成了原型驱动的内容和染色模式解耦以及感知变形对抗性学习策略的设计，旨在在保持H&E染色细节的同时精确地学习IHC染色模式。我们的方法引入了三个关键创新： 1. 原型驱动的染色模式转移，并明确内容-风格分离； 2. 一个循环注册-合成框架GapBridge，通过可变形结构对齐连接H&E和IHC领域，在此过程中，已注册特征指导跨模态风格转换而合成输出则迭代优化注册过程； 3. 感知变形的对抗性学习：我们提出了一种训练框架，在该框架中生成器与感知变形的配准网络联合对抗地优化以焦点风格为中心的判别器。 广泛的实验表明，PRINTER在保留H&E染色细节和虚拟染色保真度方面表现出了卓越性能，并且优于最先进的方法。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。
URL
https://arxiv.org/abs/2509.01214
