  Conventional hematoxylin-eosin (H&E) staining is limited to revealing cell
morphology and distribution, whereas immunohistochemical (IHC) staining
provides precise and specific visualization of protein activation at the
molecular level. Virtual staining technology has emerged as a solution for
highly efficient IHC examination, which directly transforms H&E-stained images
to IHC-stained images. However, virtual staining is challenged by the
insufficient mining of pathological semantics and the spatial misalignment of
pathological semantics. To address these issues, we propose the Pathological
Semantics-Preserving Learning method for Virtual Staining (PSPStain), which
directly incorporates the molecular-level semantic information and enhances
semantics interaction despite any spatial inconsistency. Specifically, PSPStain
comprises two novel learning strategies: 1) Protein-Aware Learning Strategy
(PALS) with Focal Optical Density (FOD) map maintains the coherence of protein
expression level, which represents molecular-level semantic information; 2)
Prototype-Consistent Learning Strategy (PCLS), which enhances cross-image
semantic interaction by prototypical consistency learning. We evaluate PSPStain
on two public datasets using five metrics: three clinically relevant metrics
and two for image quality. Extensive experiments indicate that PSPStain
outperforms current state-of-the-art H&E-to-IHC virtual staining methods and
demonstrates a high pathological correlation between the staging of real and
virtual stains.
