ReviewCytometry. Part A : the journal of the International Society for Analytical Cytology2019
Deep Learning in Image Cytometry: A Review.
Review in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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.
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Who cites it
61 citing papers in PubMed.
- Structural, Compositional, and Dielectric State Profiling in Label-Free Single-Cell Monitoring.Small methods · 2026Review
- Lightweight CycleGAN models for cross-modality image transformation and experimental quality assessment in fluorescence microscopy.Biomedical optics express · 2026Article
- AIstain: Enhancing microglial phagocytosis analysis through deep learning.Cell reports methods · 2025Article
- MIFA: Metadata, Incentives, Formats and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis.Nature methods · 2025Review
- Toxicity of Magnetic Nanoparticles in Medicine: Contributing Factors and Modern Assessment Methods.International journal of molecular sciences · 2025Review
- Microplastics and nanoplastics detection using flow cytometry: Challenges and methodological advances with fluorescent dye application.MethodsX · 2025Review
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Accurate segmentation of intracellular organelle networks using low-level features and topological self-similarity.Bioinformatics (Oxford, England) · 2024Article
- Improved microvascular imaging with optical coherence tomography using 3D neural networks and a channel attention mechanism.Scientific reports · 2024Article
- AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring.Biosensors · 2024Review
- AI-enabled organoids: Construction, analysis, and application.Bioactive materials · 2024Review
- Advanced image generation for cancer using diffusion models.Biology methods & protocols · 2024Article
- Deep Learning of Cancer Stem Cell Morphology.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Ocular condition prognosis in Keratoconus patients after corneal ring implantation using artificial neural networks.Heliyon · 2023Article
- Evaluating the utility of brightfield image data for mechanism of action prediction.PLoS computational biology · 2023Article
- Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.Scientific reports · 2023Article
- Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Quality Control of Human Pluripotent Stem Cell Colonies by Computational Image Analysis Using Convolutional Neural Networks.International journal of molecular sciences · 2022Article
- Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment.Cell reports methods · 2022Article
1 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
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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.