VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual Staining

2025-04-22

AI Summary

Existing virtual staining methods for unpaired histopathological images struggle to simultaneously achieve accurate stain style transfer and faithful preservation of pathological structures, often neglecting domain-specific pathological knowledge and the physical constraints of staining processes. Method: This paper introduces the first pathology-oriented vision-language model (VLM) into virtual staining, proposing a prompt-driven framework that jointly leverages learnable contrastive prompts, tissue foundational concept anchors, and stain-specific anchors to guide generation. We design a VLM-constrained data augmentation strategy integrating semantic understanding with physically grounded staining priors, and combine it with unpaired image-to-image translation (GAN-based) for high-fidelity stain conversion. Contribution/Results: Evaluated on multi-domain datasets, our method significantly improves perceptual realism of generated images and substantially boosts downstream task performance—e.g., glomerulus detection and segmentation accuracy. The code is publicly available.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In histopathology, tissue sections are typically stained using common H&E staining or special stains (MAS, PAS, PASM, etc.) to clearly visualize specific tissue structures. The rapid advancement of deep learning offers an effective solution for generating virtually stained images, significantly reducing the time and labor costs associated with traditional histochemical staining. However, a new challenge arises in separating the fundamental visual characteristics of tissue sections from the visual differences induced by staining agents. Additionally, virtual staining often overlooks essential pathological knowledge and the physical properties of staining, resulting in only style-level transfer. To address these issues, we introduce, for the first time in virtual staining tasks, a pathological vision-language large model (VLM) as an auxiliary tool. We integrate contrastive learnable prompts, foundational concept anchors for tissue sections, and staining-specific concept anchors to leverage the extensive knowledge of the pathological VLM. This approach is designed to describe, frame, and enhance the direction of virtual staining. Furthermore, we have developed a data augmentation method based on the constraints of the VLM. This method utilizes the VLM's powerful image interpretation capabilities to further integrate image style and structural information, proving beneficial in high-precision pathological diagnostics. Extensive evaluations on publicly available multi-domain unpaired staining datasets demonstrate that our method can generate highly realistic images and enhance the accuracy of downstream tasks, such as glomerular detection and segmentation. Our code is available at: https://github.com/CZZZZZZZZZZZZZZZZZ/VPGAN-HARBOR

Problem

• Separate tissue visual traits from staining effects
• Incorporate pathological knowledge in virtual staining
• Enhance virtual staining precision for diagnostics

Innovation

• VLM-based prompts enhance virtual staining accuracy
• Contrastive learnable anchors leverage pathological knowledge
• VLM-driven data augmentation improves image realism