Histocomponent-driven Universal Model for Virtual Immunohistochemistry Multiplex Staining via Joint Manifold Evolution

Virtual immunohistochemistry (IHC) multiplex staining hasemerged as a highly promising non-destructive solution in digital pathol-ogy. However, existing universal IHC staining models typically guide thegeneration of specific biomarkers by injecting task-specific text prompts,which often struggle to bridge the semantic gap with complex patholog-ical structures, leading to the "manifold drift" problem. Moreover, thesemodels lack specificity in modeling heterogeneous tissue components.In this paper, we propose HUSE, a Histocomponent-driven Universalmodel for virtual IHC multiplex Staining via joint manifold Evolution.HUSE introduces Joint Manifold Anchoring (JMA) as a global solution totreat H&E images as persistent structural scaffolds, effectively anchoringthe generation trajectory to the intrinsic image manifold. Complementingthis, a localized refinement scheme named Histocomponent-driven Mix-ture of Experts (Hi-MoE) and Representation Conflict Gating (RCG)is introduced to precisely capture local topological features of complexmanifolds. Furthermore, we pioneer an mIF-driven multi-marker IHCgeneration paradigm to construct large-scale 1(H&E) : N (IHC) train-ing matrices; evaluations by senior pathologists verify the feasibility ofthis paradigm as well as its exceptional clinical realism and structuralfidelity. Extensive experiments on two distinct types of datasets demon-strate that HUSE achieves state-of-the-art performance. Code is releasedat https://github.com/DeepMed-Lab-ECNU/HUSE.