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Updated: Feb 13, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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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

IEEE Transactions on Medical Imaging

|February 11, 2026

Summary

This summary is machine-generated.

This study introduces a prompt-guided framework for virtual multiplex immunohistochemical (IHC) staining, overcoming limitations in digital pathology. The method enhances H&E image analysis for more comprehensive protein expression profiling from small biopsies.

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Area of Science:

Digital pathology
Computational pathology
Biomedical imaging analysis

Background:

Immunohistochemical (IHC) staining is crucial for molecular profiling of protein expression in pathology.
Limited tissue quantity in small biopsies restricts comprehensive IHC analysis.
Virtual multiplex staining offers a digital solution but faces challenges in semantic guidance, staining consistency, and spatial alignment.

Purpose of the Study:

To develop an innovative prompt-guided framework (PGVMS) for virtual multiplex IHC staining using only uniplex training data.
To address the limitations of semantic guidance, staining distribution inconsistency, and spatial misalignment in current virtual staining methods.

Main Methods:

Developed an adaptive prompt guidance mechanism using a pathological visual language model for semantic guidance.
Implemented a protein-aware learning strategy (PALS) for precise protein expression quantification and distribution.
Utilized a prototype-consistent learning strategy (PCLS) for cross-image semantic interaction to correct spatial misalignments.

Main Results:

PGVMS demonstrated superior pathological consistency on two benchmark datasets.
The framework effectively overcomes semantic guidance, staining distribution, and spatial misalignment challenges.
Achieved high-fidelity virtual multiplex IHC staining from H&E images.

Conclusions:

PGVMS represents a significant advancement in virtual multiplex IHC staining.
The framework enables comprehensive protein profiling from limited tissue samples.
PGVMS shifts towards unified virtual staining systems, moving beyond single-task models.