Topology-aware Pathological Consistency Matching for Weakly-Paired IHC Virtual Staining

2026-01-06

arXiv.org

AI Summary

This work addresses the challenge of weakly paired supervision between adjacent H&E and IHC tissue sections, which arises from spatial misalignment and local deformations. To this end, the authors propose a topology-aware virtual staining framework that introduces two key mechanisms: Topology-Aware Consistency Matching (TACM) and Topology-Constrained Pathological Matching (TCPM). By integrating graph contrastive learning, topological perturbation, node importance analysis, and pathological region alignment, the method effectively mitigates spatial mismatches while enhancing pathological semantic consistency. Evaluated across four staining tasks on two benchmark datasets, the proposed approach consistently outperforms existing methods, yielding images of higher visual quality and greater clinical relevance.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Multi-instance/Multi-view Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• virtual staining
• weakly-paired data
• spatial misalignment
• IHC
• H&E

Innovation

• topology-aware
• virtual staining
• graph contrastive learning
• pathological consistency
• weakly-paired data