ReviewComputational and structural biotechnology journal2020
A bird's-eye view of deep learning in bioimage analysis.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed, 132 citations in OpenAlex.
- MRI image segmentation of the major lower leg muscles using deep learning: application in biomechanical analysis.Medical & biological engineering & computing · 2026Article
- Counterfactual Reasoning for Mammogram Classification via Semantic Texture Masking.Journal of imaging informatics in medicine · 2026Article
- Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope Microscopy.bioRxiv : the preprint server for biology · 2026Article
- NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.bioRxiv : the preprint server for biology · 2026Article
- Advancing dental precision: The synergy of magnification and artificial intelligence.World journal of methodology · 2025Article
- Acquisition, quality control, and architecture of a large image dataset as a tool for in silico cell biological research.Materials today. Bio · 2025Article
- bia-binder: a web-native cloud compute service for the bioimage analysis community.Bioinformatics (Oxford, England) · 2025Article
- Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence.Scientific reports · 2025Article
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
- R-AI-diographers: investigating the perceived impact of artificial intelligence on radiographers' careers, roles, and professional identity in the UK.Frontiers in digital health · 2025Article
- Fully Interpretable Deep Learning Model Using IR Thermal Images for Possible Breast Cancer Cases.Biomimetics (Basel, Switzerland) · 2024Article
- Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).European journal of medical research · 2024Article
- Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images.Scientific reports · 2024Article
- Dev-ResNet: automated developmental event detection using deep learning.The Journal of experimental biology · 2024Article
- Deep learning-based identification of spine growth potential on EOS radiographs.European radiology · 2024Article
- SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations.Briefings in bioinformatics · 2024Article
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- Harnessing artificial intelligence to reduce phototoxicity in live imaging.Journal of cell science · 2024Article
- Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.PLoS computational biology · 2024Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.
Indexed as
Identifiers
What Socratic 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 Socratic graph.