  Chemical staining methods are dependable but require extensive time,
expensive chemicals, and raise environmental concerns. These challenges
highlight the need for alternative solutions like virtual staining, which
accelerates the diagnostic process and enhances stain application flexibility.
Generative AI technologies are pivotal in addressing these issues. However, the
high-stakes nature of healthcare decisions, especially in computational
pathology, complicates the adoption of these tools due to their opaque
processes. Our work introduces the use of generative AI for virtual staining,
aiming to enhance performance, trustworthiness, scalability, and adaptability
in computational pathology. The methodology centers on a singular H&E encoder
supporting multiple stain decoders. This design focuses on critical regions in
the latent space of H&E, enabling precise synthetic stain generation. Our
method, tested to generate 8 different stains from a single H&E slide, offers
scalability by loading only necessary model components during production. We
integrate label-free knowledge in training, using loss functions and
regularization to minimize artifacts, thus improving paired/unpaired virtual
staining accuracy. To build trust, we use real-time self-inspection with
discriminators for each stain type, providing pathologists with confidence
heat-maps. Automatic quality checks on new H&E slides ensure conformity to the
trained distribution, ensuring accurate synthetic stains. Recognizing
pathologists' challenges with new technologies, we have developed an
open-source, cloud-based system, that allows easy virtual staining of H&E
slides through a browser, addressing hardware/software issues and facilitating
real-time user feedback. We also curated a novel dataset of 8 paired H&E/stains
related to pediatric Crohn's disease, comprising 480 WSIs to further stimulate
computational pathology research.
