Scalable, trustworthy generative model for virtual multi-staining from H&E whole slide images.

ABSTRACT: Chemical staining methods, while reliable, are time consuming and can be resource-intensive, involving costly chemical reagents and raising environmental concerns. This underscores the compelling need for alternative solutions such as virtual staining, which not only accelerates the diagnostic process but also enhances the flexibility of stain applications without the associated physical and chemical costs. Generative artificial intelligence technologies prove to be immensely useful in addressing these challenges. However, in healthcare, particularly within computational pathology, the high-stakes nature of decisions complicates the adoption of these tools due to their often opaque processes. Our work introduces an innovative approach that harnesses generative models for virtual stain tran

SUBMITTER: Ounissi M 

PROVIDER: S-EPMC12578341 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature