  Histopathological staining of human tissue is essential in the diagnosis of
various diseases. The recent advances in virtual tissue staining technologies
using AI alleviate some of the costly and tedious steps involved in the
traditional histochemical staining process, permitting multiplexed rapid
staining of label-free tissue without using staining reagents, while also
preserving tissue. However, potential hallucinations and artifacts in these
virtually stained tissue images pose concerns, especially for the clinical
utility of these approaches. Quality assessment of histology images is
generally performed by human experts, which can be subjective and depends on
the training level of the expert. Here, we present an autonomous quality and
hallucination assessment method (termed AQuA), mainly designed for virtual
tissue staining, while also being applicable to histochemical staining. AQuA
achieves 99.8% accuracy when detecting acceptable and unacceptable virtually
stained tissue images without access to ground truth, also presenting an
agreement of 98.5% with the manual assessments made by board-certified
pathologists. Besides, AQuA achieves super-human performance in identifying
realistic-looking, virtually stained hallucinatory images that would normally
mislead human diagnosticians by deceiving them into diagnosing patients that
never existed. We further demonstrate the wide adaptability of AQuA across
various virtually and histochemically stained tissue images and showcase its
strong external generalization to detect unseen hallucination patterns of
virtual staining network models as well as artifacts observed in the
traditional histochemical staining workflow. This framework creates new
opportunities to enhance the reliability of virtual staining and will provide
quality assurance for various image generation and transformation tasks in
digital pathology and computational imaging.
