ArticleNature communications2021
Democratising deep learning for microscopy with ZeroCostDL4Mic.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 233 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.
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
233 citing papers in PubMed, 544 citations in OpenAlex.
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- From stars to molecules: AI guided device-agnostic super-resolution imaging.Nature communications · 2026Article
- Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions.eLife · 2026Article
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.PLoS computational biology · 2026Article
- Cells Dynamically Adapt Their Nuclear Volumes and Proliferation Rates During Single to Multicellular Transitions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A quantitative pipeline for whole-mount deep imaging and analysis of multi-layered organoids across scales.eLife · 2026Article
- Deep Learning Integration in Optical Microscopy: Advancements and Applications.Microscopy research and technique · 2026Review
- Heparan sulfate is essential for Drosophila FGF export.bioRxiv : the preprint server for biology · 2026Article
- STARD3 regulates lysosome positioning and contacts via a GSK3-controlled phosphorylation switch.The EMBO journal · 2026Article
- SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.bioRxiv : the preprint server for biology · 2026Article
- Review
- Seeing the Unseen: Super-Resolution Microscopy in Protein Aggregation Research.Chemical & biomedical imaging · 2026Review
- Regulation of cell dynamics by rapid integrin transport through the biosynthetic pathway.The Journal of cell biology · 2026Article
- Artificial intelligence-powered microscopy: Transforming the landscape of parasitology.Journal of microscopy · 2026Review
- Fast label-free live imaging with FlowVision reveals key principles of cancer cell arrest on endothelial monolayers.The EMBO journal · 2026Article
- Imaging malaria parasites across scales and time.Journal of microscopy · 2026Review
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
173 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
22 authors at 11 institutions in 8 countries.
Funding
Abstract
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.
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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.