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

Area of Science:

• Computational pathology
• Artificial intelligence in medical imaging
• Digital pathology image analysis

Background:

• Histopathological staining is crucial for disease diagnosis but is costly and time-consuming.
• Virtual tissue staining using AI offers advantages like multiplexing and tissue preservation.
• Concerns exist regarding AI-induced artefacts and hallucinations in virtual staining, impacting clinical utility.

Purpose of the Study:

• To develop an autonomous quality and hallucination assessment method (AQuA) for virtual tissue staining.
• To enhance the reliability and clinical applicability of AI-driven digital pathology.
• To provide autonomous quality assurance for generated and transformed histology images.

Main Methods:

• Development of AQuA, an AI-based system for autonomous quality assessment of virtual histology images.
• Validation of AQuA's accuracy in detecting acceptable and unacceptable virtually stained images without ground truth.
• Comparison of AQuA's assessments with manual evaluations by board-certified pathologists.

Main Results:

• AQuA achieved 99.8% accuracy in autonomously assessing virtual tissue staining quality.
• AQuA demonstrated 98.5% agreement with expert pathologist assessments.
• The method successfully identified potentially misleading, realistic-looking artefacts.

Conclusions:

• AQuA significantly enhances the reliability of virtual tissue staining.
• The framework offers autonomous quality assurance for digital pathology and computational imaging.
• This technology addresses concerns about AI artefacts, improving trust in virtual histology for diagnostics.