ReviewImmunology and cell biology2017
Machine learning applications in cell image analysis.
Review in Immunology and cell biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 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.
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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
59 citing papers in PubMed.
- Integrating Environmental Exposure Profiles with Temporal Transcriptomics: An Explainable Machine Learning Model for Cardiovascular Disease Risk Prediction and Biological Validation.Cardiovascular drugs and therapy · 2026Article
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Spatial relationships between vinculin tension and actin at cell adhesions define cell colony positioning and identity.Biophysical journal · 2026Article
- Deep learning-based high-information-content graph representation of early stage bacterial biofilms.NPJ biofilms and microbiomes · 2026Article
- Radiomic Analysis as a Powerful Tool for Cytological Images of Benign Thyroid Nodules Treated by Thermal Radiofrequency Ablation.Bioengineering (Basel, Switzerland) · 2026Article
- Inductive bias influences the spatial scale of biological features learned from images.Bioinformatics advances · 2026Article
- A combined model based on clinical, radiomics, and deep transfer learning features for differentiating endometrial hyperplasia with polyps from endometrial cancer.Frontiers in oncology · 2026Article
- Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.Scientific reports · 2025Article
- A sensitive ERK fluorescent probe reveals the significance of minimal EGF-induced transcription.Cell structure and function · 2025Article
- Advancing cell therapy manufacturing: an image-based solution for accurate confluency estimation.Frontiers in bioengineering and biotechnology · 2025Article
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- AI-Assisted Cell Culture System.Methods in molecular biology (Clifton, N.J.) · 2025Article
- From pixels to connections: exploring in vitro neuron reconstruction software for network graph generation.Communications biology · 2024Review
- Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A review.Heliyon · 2024Review
- AI models for automated segmentation of engineered polycystic kidney tubules.Scientific reports · 2024Article
- Advances in the study of tertiary lymphoid structures in the immunotherapy of breast cancer.Frontiers in oncology · 2024Review
- Generative models of morphogenesis in developmental biology.Seminars in cell & developmental biology · 2023Review
- Review
- A weak-labelling and deep learning approach for in-focus object segmentation in 3D widefield microscopy.Scientific reports · 2023Article
- AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears.Journal of hematology & oncology · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) refers to a set of automatic pattern recognition methods that have been successfully applied across various problem domains, including biomedical image analysis. This review focuses on ML applications for image analysis in light microscopy experiments with typical tasks of segmenting and tracking individual cells, and modelling of reconstructed lineage trees. After describing a typical image analysis pipeline and highlighting challenges of automatic analysis (for example, variability in cell morphology, tracking in presence of clutters) this review gives a brief historical outlook of ML, followed by basic concepts and definitions required for understanding examples. This article then presents several example applications at various image processing stages, including the use of supervised learning methods for improving cell segmentation, and the application of active learning for tracking. The review concludes with remarks on parameter setting and future directions.
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28294138What 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.