ArticleLight, science & applications2019
PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning.
Article in Light, science & applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 168 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
168 citing papers in PubMed, 1 synthesis or guideline pooled it, 407 citations in OpenAlex.
- Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions.Journal of nephrology · 2024Pooled it
- Tumor-Aware Unsupervised Digital HER2 Immunohistochemistry via Dual Contrastive Learning.Biosensors · 2026Article
- Broadband backscattering confocal microscopy enables label-free 3D live cell nanoscale sensitive imaging.Science advances · 2026Article
- Hardware-Attentive Programmable Fourier Ptychography Enables Task-Adaptive Label-Free Virtual Staining.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- High-precision label-free virtual H&E staining of 3D holotomography using DAPI-guided conditional diffusion learning.International journal of computer assisted radiology and surgery · 2026Article
- Virtual immunohistochemistry by conditional generative adversarial networks.Scientific reports · 2026Article
- Rapid slide-level assessment of glomerular yield using virtual staining of unstained renal biopsy sections.Diagnostic pathology · 2026Article
- Enhancing liver fibrosis measurement: Deep learning and uncertainty analysis across multi-center cohorts.Journal of pathology informatics · 2026Article
- Label-free whole slide virtual multi-staining using dual-excitation photon absorption remote sensing microscopy.Npj imaging · 2026Article
- FPM2Stain Net: physics-guided super-resolution and multi-modal virtual staining for digital histopathology.Biomedical optics express · 2026Article
- Context-specific image quality assessment for virtual histologic staining: checklist and guideline.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Deep Learning-Enabled Virtual Multiplexed Immunostaining of Label-Free Tissue for Vascular Invasion Assessment.BME frontiers · 2026Article
- Automated HER2 Scoring with Uncertainty Quantification Using Lensfree Holography and Deep Learning.BME frontiers · 2026Article
- Mid-infrared microscopy for label-free digital staining: initial clinical assessment.Frontiers in bioengineering and biotechnology · 2026Article
- Digital pathology imaging artificial intelligence in cancer research and clinical trials: An NCI workshop report.Journal of pathology informatics · 2026Review
- MSCF-Net: unified multi-scale feature disentanglement for co-registered nucleus segmentation and virtual staining.Frontiers in cell and developmental biology · 2026Article
- Article
- A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology.Nature biomedical engineering · 2025Article
- Virtual Staining-Enabled Colorectal Cancer Metastasis Detection in Liquid Cytology Based on Digital Holography.Sensors (Basel, Switzerland) · 2025Article
108 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones' stain, and Masson's trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.