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

Journal: Nature biomedical engineering
Published Date: Jun 16, 2025

Abstract

Histopathological staining of human tissue is essential for disease diagnosis. Recent advances in virtual tissue staining technologies using artificial intelligence alleviate some of the costly and tedious steps involved in traditional histochemical staining processes, permitting multiplexed staining and tissue preservation. However, potential hallucinations and artefacts 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.

Authors

  • Luzhe Huang
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.
  • Yuzhu Li
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Nir Pillar
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Tal Keidar Haran
    Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel.
  • William Dean Wallace
    Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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