ReviewNature methods2019
Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction.
Review in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 198 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
198 citing papers in PubMed.
- Physics-informed self-supervised denoising for fluorescence microscopy via structure and intensity priors.iScience · 2026Article
- Artifact segmentation using the U-Net architecture for powder X-ray diffraction images.Journal of synchrotron radiation · 2026Article
- Label-free ovarian cancer histopathological diagnosis using two-photon autofluorescence microscopy with a joint denoising and segmentation framework.Light, science & applications · 2026Article
- From stars to molecules: AI guided device-agnostic super-resolution imaging.Nature communications · 2026Article
- Physics-guided self-supervised learning for high-fidelity reconstruction in live-cell imaging.Npj imaging · 2026Article
- Imaging-Based Spatial Transcriptomics: Data Interpretation Methods and Biomedical Applications.Biology · 2026Review
- Deep learning for high-resolution magnetic resonance vessel wall imaging: image reconstruction, stenosis diagnosis and plaque calculation.European radiology · 2026Article
- Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue.Nature communications · 2026Article
- Estimating Protein Conformational States from High-Speed AFM Images with Molecular Dynamics and Deep Learning.Journal of chemical information and modeling · 2026Article
- Physical mechanisms governing generalization and hallucination in deep learning for imaging through scattering media.Nature communications · 2026Article
- Self-Supervised Isotropic Resolution Enhancement of Expansion Microscopy via Quantized Compression.Journal of imaging informatics in medicine · 2026Article
- High-fidelity single-frame computational super-resolution using signal-preserving denoising-enabled deconvolution.Nature communications · 2026Article
- A foundation model for multi-task cross-distribution restoration of fluorescence microscopy images.Nature communications · 2026Article
- Revisiting PSF models: Unifying framework and high-performance implementation.Journal of microscopy · 2026Article
- Deep Learning-Based Event Classification of Mass Photometry Data for Optimal Mass Measurement at the Single-Molecule Level.ACS nano · 2026Article
- Self-adaptive fine-tuning of deep learning super-resolution microscopy for artifact suppression in live-cell imaging.Innovation (Cambridge (Mass.)) · 2026Article
- High-speed blind structured illumination microscopy via unsupervised algorithm unrolling.Nature communications · 2026Article
- Deep learning-enabled high-performance multiphoton fluorescence vascular imaging using clinically approved fluorescent probes.iScience · 2026Article
- A flexible framework for automated STED super-resolution microscopy.Scientific reports · 2026Article
- A neural network for long-term super-resolution imaging of live cells with reliable confidence quantification.Nature biotechnology · 2026Article
138 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is becoming an increasingly important tool for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration and super-resolution imaging, and discuss how the latest deep learning research could be applied to other image reconstruction tasks. Despite its successes, deep learning also poses substantial challenges and has limits. We discuss key questions, including how to obtain training data, whether discovery of unknown structures is possible, and the danger of inferring unsubstantiated image details.
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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.