ReviewJournal of imaging2022
A Review of Synthetic Image Data and Its Use in Computer Vision.
Review in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Thermal image segmentation in weedy fields via synthetic RGB-trained models and GAN-based cross-modality alignment.Plant phenomics (Washington, D.C.) · 2026Article
- Multimodal AI for Real-Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation.Food science & nutrition · 2026Review
- Machine Learning Modeling for Predicting Infliximab Pharmacokinetics in Pediatric and Young Adult Patients With Crohn Disease: Leveraging Ensemble Modeling With Synthetic and Real-World Data.Therapeutic drug monitoring · 2026Article
- Markov Chain Wave Generative Adversarial Network for Bee Bioacoustic Signal Synthesis.Sensors (Basel, Switzerland) · 2026Article
- A review of image processing and analysis of computed tomography images using deep learning methods.Physical and engineering sciences in medicine · 2025Review
- MetaAcuPoint: MetaHuman-Generated Synthetic Data for Hand Acupoint Localization.Healthcare (Basel, Switzerland) · 2025Article
- Pursuing Better Representations: Balancing Discriminability and Transferability for Few-Shot Class-Incremental Learning.Journal of imaging · 2025Article
- A reproducible framework for synthetic data generation and instance segmentation in robotic suturing.International journal of computer assisted radiology and surgery · 2025Article
- Validity of tremor analysis using smartphone compatible computer vision frameworks.Scientific reports · 2025Article
- Deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data.Communications medicine · 2025Article
- SinGAN-CBAM: a multi-scale GAN with attention for few-shot plant disease image generation.Frontiers in plant science · 2025Article
- From blender to farm: Transforming controlled environment agriculture with synthetic data and SwinUNet for precision crop monitoring.PloS one · 2025Article
- Comparison of human observer impression of X-ray fluoroscopy and angiography image quality with technical changes to image quality.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Finetuning of GLIDE stable diffusion model for AI-based text-conditional image synthesis of dermoscopic images.Frontiers in medicine · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of computer vision algorithms using convolutional neural networks and deep learning has necessitated ever greater amounts of annotated and labelled data to produce high performance models. Large, public data sets have been instrumental in pushing forward computer vision by providing the data necessary for training. However, many computer vision applications cannot rely on general image data provided in the available public datasets to train models, instead requiring labelled image data that is not readily available in the public domain on a large scale. At the same time, acquiring such data from the real world can be difficult, costly to obtain, and manual labour intensive to label in large quantities. Because of this, synthetic image data has been pushed to the forefront as a potentially faster and cheaper alternative to collecting and annotating real data. This review provides general overview of types of synthetic image data, as categorised by synthesised output, common methods of synthesising different types of image data, existing applications and logical extensions, performance of synthetic image data in different applications and the associated difficulties in assessing data performance, and areas for further research.
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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.