Diffusion-based cross-staining feature transformation for whole slide image analysis: From H&E to IHC representation learning.

Abstract

In computational pathology, Hematoxylin and Eosin (H&E) staining offers a cost-effective solution for tissue analysis, while Immunohistochemistry (IHC) delivers specific biomarker expression at substantially higher cost and operational complexity. Existing H&E-to-IHC translation methods predominantly operate at the pixel level, often overlooking the preservation of high-level semantic features required by modern multi-instance learning frameworks. To bridge this gap, we present FeatStainDiff, a diffusion-based model that performs direct feature-level transformation between staining modalities. Our framework incorporates two novel components: a Contrastive Semantic Bridging mechanism that ensures diagnostic semantics are preserved during cross-modal translation, and a Frequency-domain Mixture of Experts module that adaptively handles distribution shifts through spectral processing. This design enables the generation of high-fidelity and pathologically consistent IHC features directly from H&E inputs. Through extensive evaluation on two virtual staining datasets and two whole-slide image classification benchmarks, we demonstrate that FeatStainDiff consistently surpasses existing approaches. The method achieves significant improvements in feature similarity metrics, while downstream classification tasks benefit from markedly enhanced performance. FeatStainDiff provides an effective and practical pathway for computational biomarker prediction, with promising potential to expand access to specialized staining analysis in resource-limited clinical environments. Code will be made publicly available upon publication.