Ensuring accurate stain reproduction in deep generative networks for virtual immunohistochemistry. (arXiv:2204.06849v1 [eess.IV])

Immunohistochemistry is a valuable diagnostic tool for cancer pathology.
However, it requires specialist labs and equipment, is time-intensive, and is
difficult to reproduce. Consequently, a long term aim is to provide a digital
method of recreating physical immunohistochemical stains. Generative
Adversarial Networks have become exceedingly advanced at mapping one image type
to another and have shown promise at inferring immunostains from haematoxylin
and eosin. However, they have a substantial weakness when used with pathology
images as they can fabricate structures …

arxiv deep generative networks networks virtual