# Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks

arXiv: https://arxiv.org/abs/2207.02946 · HTML: https://mlsanity.com/paper/2207.02946/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue](https://mlsanity.com/paper/1803.11293/) (2018)
- [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018)
- [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)
- [Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning](https://mlsanity.com/paper/1901.11252/) (2019)
- [Single-shot autofocusing of microscopy images using deep learning](https://mlsanity.com/paper/2003.09585/) (2020)
- [Deep learning enhanced mobile-phone microscopy](https://mlsanity.com/paper/1712.04139/) (2017)
- [Recurrent neural network-based volumetric fluorescence microscopy](https://mlsanity.com/paper/2010.10781/) (2020)
- [Deep learning-based virtual refocusing of images using an engineered point-spread function](https://mlsanity.com/paper/2012.11892/) (2020)
- [Autofocusing technologies for whole slide imaging and automated microscopy](https://mlsanity.com/paper/2006.08610/) (2020)
- [Rapid focus map surveying for whole slide imaging with continues sample motion](https://mlsanity.com/paper/1707.03039/) (2017)

## Built on by (most cited first, 11 of 11)

- [Deep Learning-enabled Virtual Histological Staining of Biological Samples](https://mlsanity.com/paper/2211.06822/) (2022)
- [Self-supervised learning of hologram reconstruction using physics consistency](https://mlsanity.com/paper/2209.08288/) (2022)
- [Virtual histological staining of unlabeled autopsy tissue](https://mlsanity.com/paper/2308.00920/) (2023)
- [Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning](https://mlsanity.com/paper/2403.09100/) (2024)
- [Pixel super-resolved virtual staining of label-free tissue using diffusion models](https://mlsanity.com/paper/2410.20073/) (2024)
- [Digital staining in optical microscopy using deep learning -- a review](https://mlsanity.com/paper/2303.08140/) (2023)
- [A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology](https://mlsanity.com/paper/2404.18458/) (2024)
- [GANs vs. Diffusion Models for virtual staining with the HER2match dataset](https://mlsanity.com/paper/2506.18484/) (2025)
- [Label-free evaluation of lung and heart transplant biopsies using tissue autofluorescence-based virtual staining](https://mlsanity.com/paper/2409.05255/) (2024)
- [Deep learning-enabled virtual multiplexed immunostaining of label-free tissue for vascular invasion assessment](https://mlsanity.com/paper/2508.16209/) (2025)
- [Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning](https://mlsanity.com/paper/2407.12337/) (2024)

## Abstract (arXiv)

Deep learning-based virtual staining was developed to introduce image contrast to label-free tissue sections, digitally matching the histological staining, which is time-consuming, labor-intensive, and destructive to tissue. Standard virtual staining requires high autofocusing precision during the whole slide imaging of label-free tissue, which consumes a significant portion of the total imaging time and can lead to tissue photodamage. Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope's autofocusing precision. This framework incorporates a virtual-autofocusing neural network to digitally refocus the defocused images and then transforms the refocused images into virtually stained images using a successive network. These cascaded networks form a collaborative inference scheme: the virtual staining model regularizes the virtual-autofocusing network through a style loss during the training. To demonstrate the efficacy of this framework, we trained and blindly tested these networks using human lung tissue. Using 4x fewer focus points with 2x lower focusing precision, we successfully transformed the coarsely-focused autofluorescence images into high-quality virtually stained H&E images, matching the standard virtual staining framework that used finely-focused autofluorescence input images. Without sacrificing the staining quality, this framework decreases the total image acquisition time needed for virtual staining of a label-free whole-slide image (WSI) by ~32%, together with a ~89% decrease in the autofocusing time, and has the potential to eliminate the laborious and costly histochemical staining process in pathology.