Prompting Beyond Pairs: Decoupled Semantic Supervision for Knowledge-Guided Multiplex Virtual Staining
This paper introduces a novel virtual staining framework that decouples structural guidance from image translation using domain-knowledge prompts and self-supervised learning, enabling high-fidelity, flexible synthesis of multiple subcellular structures from single-channel data without relying on rigidly paired multiplex fluorescence targets.