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

TL;DR

This paper introduces a topology-aware framework for virtual IHC staining from H&E images, effectively handling weakly-paired data with spatial misalignments through graph contrastive learning and topological constraints.

Contribution

It proposes a novel topology-aware consistency matching mechanism and a topology-constrained pathological matching method for improved virtual staining accuracy.

Findings

Outperforms state-of-the-art methods on multiple benchmarks

Achieves higher clinical relevance in generated images

Demonstrates robustness to spatial misalignments

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.