Context-Aware Contrastive Learning for Virtual IHC Staining with Inconsistent Image Pairs

In recent years, virtual immunohistochemical (IHC) staining, which converts hematoxylin and eosin (H&E) images into IHC images, has emerged as a promising technology in digital histopathology. Most existing methods rely on paired H&E and IHC patches extracted from adjacent tissue sections for supervised training. However, tissue misalignment and tissue loss between adjacent sections lead to inconsistent training pairs, limiting the models' ability to produce accurate staining results. To address this issue, we propose ConCLR, a two-stage virtual IHC staining framework based on context-aware contrastive learning, designed to handle inconsistently paired patches. Our method is built on the assumption that for a given mini-patch in the H&E patch, there may exist a corresponding mini-patch in the reference IHC patch exhibiting a similar Pos/Neg pathological pattern. If such a mini-patch exists, it is typically located spatially close to the H&E mini-patch due to the local consistency of tissue structure. In the first stage, we leverage this assumption to design a similarity-guided mini-patch sampling (SGMS) module. For each mini-patch anchor in the staining results, SGMS searches within the real IHC patch to find the most similar mini-patch to serve as the positive sample for contrastive learning, enabling effective supervision despite mild tissue misalignment. In the second stage, we design a context-aware adaptive refinement module, which addresses significant inconsistencies between training pairs caused by potential tissue loss, by expanding the search range of positive samples to include neighboring patches. Extensive experiments on two network backbones across four virtual IHC staining tasks demonstrate the effectiveness of our ConCLR. Evaluations include qualitative and quantitative assessments of staining results, as well as downstream diagnostic performance. In addition to experiments on existing public datasets, we collected a PanCK-NSCLC dataset by acquiring H&E and pancytokeratin staining images from the same lung tissue sections via destaining and restaining. This dataset offers significantly improved tissue alignment compared to those derived from adjacent sections, with the aim of facilitating further progress in virtual IHC staining.