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

arXiv: https://arxiv.org/abs/2404.18458 · HTML: https://mlsanity.com/paper/2404.18458/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 78 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [Deep learning-based transformation of the H&E stain into special stains](https://mlsanity.com/paper/2008.08871/) (2020)
- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018)
- [Learning to Generate Images with Perceptual Similarity Metrics](https://mlsanity.com/paper/1511.06409/) (2015)
- [Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue](https://mlsanity.com/paper/2001.07267/) (2020)
- [Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning](https://mlsanity.com/paper/2112.05240/) (2021)
- [CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection](https://mlsanity.com/paper/2012.02374/) (2020)
- [Self-supervised learning of hologram reconstruction using physics consistency](https://mlsanity.com/paper/2209.08288/) (2022)
- [Virtual stain transfer in histology via cascaded deep neural networks](https://mlsanity.com/paper/2207.06578/) (2022)
- [Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks](https://mlsanity.com/paper/2207.02946/) (2022)
- [Virtual histological staining of unlabeled autopsy tissue](https://mlsanity.com/paper/2308.00920/) (2023)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)
- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems](https://mlsanity.com/paper/2305.12852/) (2023)

## Built on by (most cited first, 6 of 6)

- [Universal and Transferable Attacks on Pathology Foundation Models](https://mlsanity.com/paper/2510.16660/) (2025)
- [Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment](https://mlsanity.com/paper/2508.16209/) (2025)
- [Hallucination Detection in Virtually-Stained Histology: A Latent Space Baseline](https://mlsanity.com/paper/2411.15060/) (2024)
- [Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning](https://mlsanity.com/paper/2512.16266/) (2025)
- [Radiology Report Generation for Low-Quality X-Ray Images](https://mlsanity.com/paper/2604.10188/) (2026)
- [On Hallucinations in Inverse Problems: Fundamental Limits and Provable Assessment Methods](https://mlsanity.com/paper/2605.13146/) (2026)

## 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.