mlsanityData to 7 Oct 2026

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

arXiv 2404.18458 ↗First posted 29 Apr 2024eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 27 of its 78 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

27 of 78 references.

References · 27

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15 more
  1. 2020Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systemscited by 65
  2. 2020Deep learning-based transformation of the H&E stain into special stainscited by 55
  3. 2022Deep Learning-enabled Virtual Histological Staining of Biological Samplescited by 42
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2025Universal and Transferable Attacks on Pathology Foundation Modelscited by 2
  2. 2025Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessmentcited by 1
  3. 2024Hallucination Detection in Virtually-Stained Histology: A Latent Space Baselinecited by 0
  4. 2025Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learningcited by 0
  5. 2026Radiology Report Generation for Low-Quality X-Ray Imagescited by 0
  6. 2026On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methodscited by 0