ReviewBiomedical optics express2020
Deep learning in single-molecule microscopy: fundamentals, caveats, and recent developments [Invited].
Review in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed.
- Diffusing caveolin-1 scaffolds regulate mechanosignalling.Nature cell biology · 2026Article
- Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.Nature communications · 2026Article
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- Label-free TIMING: an efficient, reliable and scalable AI workflow for automated profiling of cell-cell interaction behaviors in nanowell arrays.Frontiers in bioinformatics · 2026Article
- A Bayesian approach towards atomically-precise localization in fluorescence microscopy.Nature communications · 2025Article
- 3D localization of retrovirus assembly in the presence of structured background with deep learning.Biophysical journal · 2025Article
- DHPSFU: a Fiji plugin for fast and accurate double helix-PSF 3D single-molecule localisation microscopy.Scientific reports · 2025Article
- Enhancing ultra-high density single-molecule localization with deep spatiotemporal networks.Biomedical optics express · 2025Article
- Super-Resolution Mapping and Quantification of Molecular Diffusion via Single-Molecule Displacement/Diffusivity Mapping (SMAccounts of chemical research · 2025Article
- Resolving the Orientations of and Angular Separation Between a Pair of Dipole Emitters.Physical review letters · 2025Article
- Optical Microscopy and Deep Learning for Absolute Quantification of Nanoparticles on a Macroscopic Scale and Estimating Their Number Concentration.Analytical chemistry · 2025Article
- Single-molecule orientation-localization microscopy: Applications and approaches.Quarterly reviews of biophysics · 2024Review
- Deep learning based local feature classification to automatically identify single molecule fluorescence events.Communications biology · 2024Article
- Deep learning-based spectroscopic single-molecule localization microscopy.Journal of biomedical optics · 2024Article
- Trustworthy in silico cell labeling via ensemble-based image translation.Biophysical reports · 2023Article
- DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning.Nature methods · 2023Article
- Polarization differential interference contrast microscopy with physics-inspired plug-and-play denoiser for single-shot high-performance quantitative phase imaging.Biomedical optics express · 2023Article
- Article
- Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.Development (Cambridge, England) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Deep learning-based data analysis methods have gained considerable attention in all fields of science over the last decade. In recent years, this trend has reached the single-molecule community. In this review, we will survey significant contributions of the application of deep learning in single-molecule imaging experiments. Additionally, we will describe the historical events that led to the development of modern deep learning methods, summarize the fundamental concepts of deep learning, and highlight the importance of proper data composition for accurate, unbiased results.
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.