ArticleNature biomedical engineering2025
A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Hardware-Attentive Programmable Fourier Ptychography Enables Task-Adaptive Label-Free Virtual Staining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Optical digital twins for disease prevention, diagnosis, therapy, and intervention.Journal of biomedical optics · 2026Review
- Deep-Learning-Driven High-Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon-Limited Conditions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in pathology: a framework for preserving brain capital in the diagnostic apex.Croatian medical journal · 2026Article
- Universal and transferable attacks on pathology foundation models using microscopic perturbations.Light, science & applications · 2026Article
- CARE framework: Rethinking randomized controlled trials for medical artificial intelligence.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- On Hallucinations in Artificial Intelligence-Generated Content for Nuclear Medicine Imaging (the DREAM Report).Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Review
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Deep Learning-Enabled Virtual Multiplexed Immunostaining of Label-Free Tissue for Vascular Invasion Assessment.BME frontiers · 2026Article
- ViT-Stain: Vision transformer-driven virtual staining for skin histopathology via global contextual learning.PloS one · 2026Article
- Article
- Computational Pathology with Topological signatures and Visual Word Encoding.Research square · 2025Article
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6 authors.
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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.
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