A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology

TL;DR

This paper introduces AQuA, an autonomous AI-based method for assessing the quality of virtual tissue stained images, achieving high accuracy and agreement with expert pathologists, thereby improving reliability in digital pathology.

Contribution

The paper presents AQuA, a novel autonomous quality assessment framework for virtual tissue staining that does not require ground truth and matches expert evaluations.

Findings

AQuA detects acceptable and unacceptable images with 99.8% accuracy.

AQuA agrees with pathologists' assessments at 98.5%.

AQuA is adaptable across various tissue staining methods.

Abstract

Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence (AI) alleviate some of the costly and tedious steps involved in traditional histochemical staining processes, permitting multiplexed staining and tissue preservation. However, potential hallucinations and artifacts in these virtually stained tissue images pose concerns, especially for the clinical uses of these approaches. Quality assessment of histology images by experts can be subjective. Here, we present an autonomous quality and hallucination assessment method, AQuA, for virtual tissue staining and digital pathology. AQuA autonomously achieves 99.8% accuracy when detecting acceptable and unacceptable virtually stained tissue images without access to histochemically stained ground truth, and presents an agreement of 98.5% with the manual assessments made by board-certified pathologists, including identifying realistic-looking images that could mislead diagnosticians. We demonstrate the wide adaptability of AQuA across various virtually and histochemically stained human tissue images. This framework enhances the reliability of virtual tissue staining and provides autonomous quality assurance for image generation and transformation tasks in digital pathology and computational imaging.