ArticleNature communications2025
Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy.
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 6 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
6 citing papers in PubMed.
- Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy.Methods and applications in fluorescence · 2026Article
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- Aberration-aware 3D localization microscopy via self-supervised neural-physics learning.Nature communications · 2026Article
- Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data.Cells · 2026Review
- CC-DenseSTORM: deep learning enables colorimetry camera-based simultaneous two-color single-molecule localization microscopy with dense emitters.Biomedical optics express · 2026Article
- Scalable and lightweight deep learning for efficient high accuracy single-molecule localization microscopy.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Deep learning has significantly improved the performance of single-molecule localization microscopy (SMLM), but many existing methods remain computationally intensive, limiting their applicability in high-throughput settings. To address these challenges, we present LiteLoc, a scalable analysis framework for high-throughput SMLM data analysis. LiteLoc employs a lightweight neural network architecture and integrates parallel processing across central processing unit (CPU) and graphics processing unit (GPU) resources to reduce latency and energy consumption without sacrificing localization accuracy. LiteLoc demonstrates substantial gains in processing speed and resource efficiency, making it an effective and scalable tool for routine SMLM workflows in biological research.
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.