ABSTRACT
Histological staining is vital in clinical pathology for visualizing tissue structures. However, these techniques are laborious and time-consuming. Digital virtual staining offers a promising solution, but existing methods typically rely on Generative Adversarial Networks (GANs), which may suffer from artifacts and mode collapse. Motivated by the success of diffusion models, we present DUST, a novel Diffusion-based Unified framework for versatile Stain Transfer in histopathology. To enhance domain awareness and task-specific performance, we propose a dual encoding strategy that integrates the stain types of both the source and target domains. Additionally, we introduce a dynamic dual-output head to address the unstable intensity issue encountered with conventional DDPM implementations. Validated on a curated fourstain kidney histopathological dataset (H&E, MT, PAS, and PASM), DUST demonstrates superior versatile stain transfer capabilities. Our research highlights the potential of diffusion models to advance virtual staining, paving the way for more efficient digital pathology analyses.
Competing Interest Statement
The authors have declared no competing interest.
