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PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning

Fuqiang Chen, Ranran Zhang, Wanming Hu, Deboch Eyob Abera, Yue Peng, Boyun Zheng, Yiwen Sun, Jing Cai, Wenjian Qin

TL;DR

This work presents a prompt-guided framework for virtual multiplex IHC staining using only uniplex training data (PGVMS), which represents a paradigm shift from dedicated single-task models toward unified virtual staining systems.

Abstract

Immunohistochemical (IHC) staining enables precise molecular profiling of protein expression, with over 200 clinically available antibody-based tests in modern pathology. However, comprehensive IHC analysis is frequently limited by insufficient tissue quantities in small biopsies. Therefore, virtual multiplex staining emerges as an innovative solution to digitally transform H&E images into multiple IHC representations, yet current methods still face three critical challenges: (1) inadequate semantic guidance for multi-staining, (2) inconsistent distribution of immunochemistry staining, and (3) spatial misalignment across different stain modalities. To overcome these limitations, we present a prompt-guided framework for virtual multiplex IHC staining using only uniplex training data (PGVMS). Our framework introduces three key innovations corresponding to each challenge: First, an adaptive prompt guidance mechanism employing a pathological visual language model dynamically adjusts staining prompts to resolve semantic guidance limitations (Challenge 1). Second, our protein-aware learning strategy (PALS) maintains precise protein expression patterns by direct quantification and constraint of protein distributions (Challenge 2). Third, the prototype-consistent learning strategy (PCLS) establishes cross-image semantic interaction to correct spatial misalignments (Challenge 3).

PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning

TL;DR

This work presents a prompt-guided framework for virtual multiplex IHC staining using only uniplex training data (PGVMS), which represents a paradigm shift from dedicated single-task models toward unified virtual staining systems.

Abstract

Immunohistochemical (IHC) staining enables precise molecular profiling of protein expression, with over 200 clinically available antibody-based tests in modern pathology. However, comprehensive IHC analysis is frequently limited by insufficient tissue quantities in small biopsies. Therefore, virtual multiplex staining emerges as an innovative solution to digitally transform H&E images into multiple IHC representations, yet current methods still face three critical challenges: (1) inadequate semantic guidance for multi-staining, (2) inconsistent distribution of immunochemistry staining, and (3) spatial misalignment across different stain modalities. To overcome these limitations, we present a prompt-guided framework for virtual multiplex IHC staining using only uniplex training data (PGVMS). Our framework introduces three key innovations corresponding to each challenge: First, an adaptive prompt guidance mechanism employing a pathological visual language model dynamically adjusts staining prompts to resolve semantic guidance limitations (Challenge 1). Second, our protein-aware learning strategy (PALS) maintains precise protein expression patterns by direct quantification and constraint of protein distributions (Challenge 2). Third, the prototype-consistent learning strategy (PCLS) establishes cross-image semantic interaction to correct spatial misalignments (Challenge 3).
Paper Structure (50 sections, 28 equations, 19 figures, 15 tables)

This paper contains 50 sections, 28 equations, 19 figures, 15 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Image-to-Image Translation
  4. Virtual Staining
  5. Pathological Visual Language Model
  6. Method
  7. Pathological Semantics-Style Guided Generator
  8. Protein-Aware Learning Strategy
  9. Focal Optical Density Mapping
  10. Multi-Level Protein Awareness
  11. Prototype-Consistent Learning Strategy
  12. Protein Prototype Extraction
  13. Cross-image Protein Prototype Consistency
  14. Other Loss Function
  15. Overall Loss Function
  16. ...and 35 more sections

Figures (19)

  • Figure 1: The comparison of traditional IHC staining and virtual staining workflows. From (a) to (c), they are conventional IHC staining, biomarker-specific virtual staining and our PGVMS.
  • Figure 2: Three challenges of virtual multi-staining. (a) Inadequate semantic guidance for multi-staining. (b) Inconsistent distribution of immunochemistry staining. (c) Spatial misalignment of pathological semantics.
  • Figure 3: The framework of PGVMS. (a) Overall architecture of PGVMS, including the pathological semantics--style guided (PSSG) generator and schematic overviews of the protein-aware learning strategy (PALS) and the prototype-consistent learning strategy (PCLS). (b) and (c) show detailed illustrations of PALS and PCLS in the bottom row, highlighting their key components and operations.
  • Figure 4: Virtual IHC-stained image results of different methods on the MIST dataset. The first column is the H&E-stained images. Columns 2 to 14 are the images virtually stained by different methods. The last column is the ground truth. The figure also demonstrates virtual IHC results alongside positive cell visualization from DeepLIIF.
  • Figure 5: Consistent with the protein progression tendency. The horizontal axis (abscissa) represents the sample index, while the vertical axis (ordinate) denotes the accumulated optical density (OD) value. The figure illustrates the accumulated OD curves of various virtual staining methods for IHC positive regions, comparing the virtual results with the reference IHC images on the MIST and IHC4BC datasets. The results of the ControlNet model are excluded because they deviate significantly from the ground truth.
  • ...and 14 more figures