ArticlePloS one2023
Domain-guided data augmentation for deep learning on medical imaging.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
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- MViT: A vision transformer with fractal path reordering and dynamic positional encoding.PloS one · 2026Article
- Optimizing anisotropic margins in single-isocenter multiple brain metastases radiosurgery using regressor strategies: A multi-institutional validation study.Journal of applied clinical medical physics · 2025Article
- Development of an AI-Assisted System for Automatic Recognition and Localization Marking of Colonic Polyps (With Video).Journal of gastroenterology and hepatology · 2025Article
- Machine Learning to Predict Outcomes of Fetal Cardiac Disease: A Pilot Study.Pediatric cardiology · 2025Article
- Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video).BMC medical informatics and decision making · 2025Article
- PixMed-Enhancer: An Efficient Approach for Medical Image Augmentation.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective Visual Evaluation.Diagnostics (Basel, Switzerland) · 2025Article
- A YOLOv11-based AI system for keypoint detection of auricular acupuncture points in traditional Chinese medicine.Frontiers in physiology · 2025Article
- Establishing an AI model and application for automated capsule endoscopy recognition based on convolutional neural networks (with video).BMC gastroenterology · 2024Article
- Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (Quantitative imaging in medicine and surgery · 2024Article
- Real-time deep-learning NICE classification and withdrawal-speed monitoring for colorectal endoscopy.Digital healthArticle
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Authors and funding
2 authors.
Funding
Abstract
While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.
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