StainExpert: A Unified Multi-Expert Diffusion Framework for Multi-Target Pathological Stain Translation.

Area of Science:

• Computational pathology
• Digital pathology
• Artificial intelligence in medicine

Background:

• Histopathological analysis is crucial for disease diagnosis, using various staining methods like hematoxylin and eosin (H&E), special stains, immunohistochemistry (IHC), and multiplex immune-fluorescence (mpIF).
• Current sequential staining methods are time-consuming, labor-intensive, consume significant tissue, and can compromise sample integrity due to serial sectioning.

Purpose of the Study:

• To introduce StainExpert, a unified multimodal diffusion framework for translating a single source pathological stain image into multiple target stain images.
• To develop a novel multi-expert system that enables efficient, single-source to multi-target stain translation, overcoming limitations of single-pair models.

Main Methods:

• Developed StainExpert, a multimodal diffusion framework with a multi-expert system for collaborative learning of staining principles.
• Employed multi-expert and multi-objective optimization for efficient source-to-multi-target translation.
• Integrated textual guidance with visual features within a multimodal diffusion architecture.

Main Results:

• StainExpert successfully generated high-quality virtual stains across H&E, special stains, IHC, and mpIF modalities on three datasets.
• The generated virtual stains preserved critical pathological features essential for accurate diagnosis.
• The framework demonstrated robust cross-domain generalization capabilities.

Conclusions:

• StainExpert offers a transformative platform for efficient multi-target pathological stain translation.
• The tool advances computational pathology towards streamlined, tissue-conserving, and resource-efficient diagnostic workflows.
• This approach enhances diagnostic accuracy and efficiency in histopathology.