ReviewJournal of imaging2023
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review.
Review in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Article
- Multimodal transformer-enhanced deep learning for accurate assessment of mandibular third molar-inferior alveolar canal contact using panoramic radiography and CBCT slices.BMC oral health · 2026Article
- Enhancing brain tumor detection through deep learning and explainable AI techniques.Scientific reports · 2026Article
- StyleGAN-based synthetic image augmentation for multi-class otoscopy image classification.Scientific reports · 2026Article
- The accuracy-fairness-efficiency Trilemma in mobile image classification: a Pareto benchmark.Scientific reports · 2026Article
- Generative AI-Assisted Indication of Anatomical Landmarks to Enhance Safety under Intraoperative Bleeding in Laparoscopic Gastrectomy for Gastric Cancer.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- SULBA: A Task-Agnostic Data Augmentation Framework for Deep Learning in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Modelling Stochastic Sensor Noise via Mask-Conditioned Diffusion for Data Augmentation in Low-SNR LGE-CMR.Sensors (Basel, Switzerland) · 2026Article
- A solid waste classification using reinforcement learning boosted by data augmentation and hyperparameter optimization.Scientific reports · 2026Article
- Anomaly Detection for Structural and Functional Connectivity in Glioma Patients.NMR in biomedicine · 2026Article
- Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine.PLoS computational biology · 2026Article
- Exploring Deep Learning Models for Small Histopathology Datasets: Segmentation and Classification of Glomerular Crescent Lesions with Ablation, Interpretability, and Calibration Analyses.Interdisciplinary sciences, computational life sciences · 2026Article
- Impact of data space augmentation strategy on model accuracy and generalization in thin-section rock classification.Scientific reports · 2026Article
- AI-enabled RF data synthesis for breast ultrasound: efficacy in quantitative ultrasound tissue characterization.Scientific reports · 2026Article
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Diffusion models vs. DCGANs for class-imbalanced lung cancer CT classification: A comparative study.Intelligence-based medicine · 2026Article
- MedScanGAN: Synthetic PET & CT Scan Generation Using Conditional Generative Adversarial Networks for Medical AI Data Augmentation.Bioengineering (Basel, Switzerland) · 2026Article
12 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing the number of training samples, but these techniques often produce limited and unconvincing results. To address this issue, a growing number of studies have proposed the use of deep generative models to generate more realistic and diverse data that conform to the true distribution of the data. In this review, we focus on three types of deep generative models for medical image augmentation: variational autoencoders, generative adversarial networks, and diffusion models. We provide an overview of the current state of the art in each of these models and discuss their potential for use in different downstream tasks in medical imaging, including classification, segmentation, and cross-modal translation. We also evaluate the strengths and limitations of each model and suggest directions for future research in this field. Our goal is to provide a comprehensive review about the use of deep generative models for medical image augmentation and to highlight the potential of these models for improving the performance of deep learning algorithms in medical image analysis.
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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.