ArticleNature communications2025
Real-time self-supervised denoising for high-speed fluorescence neural imaging.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Physics-informed self-supervised denoising for fluorescence microscopy via structure and intensity priors.iScience · 2026Article
- CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies.Nature communications · 2026Article
- Enhancing biomedical optical volumetric imaging via self-supervised orthogonal learning.Science advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Self-supervised denoising methods significantly enhance the signal-to-noise ratio in fluorescence neural imaging, yet real-time solutions remain scarce in high-speed applications. Here, we present the FrAme-multiplexed SpatioTemporal learning strategy (FAST), a deep-learning framework designed for high-speed fluorescence neural imaging, including in vivo calcium, voltage, and volumetric time-lapse imaging. FAST balances spatial and temporal redundancy across neighboring pixels, preserving structural fidelity while preventing over-smoothing of rapidly evolving fluorescence signals. Utilizing an ultra-light convolutional neural network, FAST enables real-time processing at speeds exceeding 1000 frames per second, substantially surpassing the acquisition rates of most high-speed imaging systems. We also introduce an intuitive graphical user interface that integrates FAST into standard imaging workflows, providing a real-time denoising tool for recorded neural activity and enabling downstream analysis in neuroscience research that requires millisecond-scale temporal precision, particularly in closed-loop studies.
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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.