Immunohistochemical (IHC) staining provides crucial molecular characterization of tissue samples and plays an indispensable role in the clinical examination and diagnosis of cancers. However, compared with the commonly used Hematoxylin and Eosin (H&E) staining, IHC staining involves complex procedures and is both time-consuming and expensive, which limits its widespread clinical use. Virtual staining converts H&E images to IHC images, offering a cost-effective alternative to clinical IHC staining. Nevertheless, using adjacent slides as ground truth often results in weakly-paired data with spatial misalignment and local deformations, hindering effective supervised learning. To address these challenges, we propose a novel topology-aware framework for H&E-to-IHC virtual staining. Specifically, we introduce a Topology-aware Consistency Matching (TACM) mechanism that employs graph contrastive learning and topological perturbations to learn robust matching patterns despite spatial misalignments, ensuring structural consistency. Furthermore, we propose a Topology-constrained Pathological Matching (TCPM) mechanism that aligns pathological positive regions based on node importance to enhance pathological consistency. Extensive experiments on two benchmarks across four staining tasks demonstrate that our method outperforms state-of-the-art approaches, achieving superior generation quality with higher clinical relevance.

翻译：免疫组化染色为组织样本提供了关键的分子表征，在癌症的临床检查与诊断中发挥着不可或缺的作用。然而，与常用的苏木精-伊红染色相比，免疫组化染色流程复杂、耗时且昂贵，限制了其广泛的临床应用。虚拟染色技术可将H&E图像转换为免疫组化图像，为临床免疫组化染色提供了一种经济高效的替代方案。然而，使用相邻切片作为真实标签通常会产生具有空间错位和局部形变的弱配对数据，阻碍了有效的监督学习。为应对这些挑战，我们提出了一种新颖的拓扑感知框架用于H&E到免疫组化的虚拟染色。具体而言，我们引入了一种拓扑感知一致性匹配机制，该机制利用图对比学习和拓扑扰动来学习鲁棒的匹配模式，从而在存在空间错位的情况下确保结构一致性。此外，我们提出了一种拓扑约束病理匹配机制，该机制基于节点重要性对齐病理阳性区域，以增强病理一致性。在两个基准数据集上针对四种染色任务进行的广泛实验表明，我们的方法优于现有最先进的方法，实现了具有更高临床相关性的优异生成质量。