In pathological examinations

tissue must first be stained to meet specific diagnostic requirements

a meticulous process demanding significant time and expertise from specialists. With advancements in deep learning

this staining process can now be achieved through computational methods known as virtual staining. This technique replicates the visual effects of traditional histological staining in pathological imaging

enhancing efficiency and reducing costs. Extensive research in virtual staining for pathology has already demonstrated its effectiveness in generating clinically relevant stained images across a variety of diagnostic scenarios. Unlike previous reviews that broadly cover the clinical applications of virtual staining

this paper focuses on the technical methodologies

encompassing current models

datasets

and evaluation methods. It highlights the unique challenges of virtual staining compared to traditional image translation

discusses limitations in existing work

and explores future perspectives. Adopting a macro perspective

we avoid overly intricate technical details to make the content accessible to clinical experts. Additionally

we provide a brief introduction to the purpose of virtual staining from a medical standpoint

which may inspire algorithm-focused researchers. This paper aims to promote a deeper understanding of interdisciplinary knowledge between algorithm developers and clinicians

fostering the integration of technical solutions and medical expertise in the development of virtual staining models. This collaboration seeks to create more efficient

generalized

and versatile virtual staining models for a wide range of clinical applications.