ReviewRadiology. Artificial intelligence2021
Training Strategies for Radiology Deep Learning Models in Data-limited Scenarios.
Review in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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.
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
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Few-Shot Learning for Cytological Classification of Primary Lung Cancer and Pulmonary Metastases During Endobronchial Ultrasound.Diagnostics (Basel, Switzerland) · 2026Article
- Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.European radiology · 2026Article
- Multimodal Deep Learning Approaches for Lung Disease Detection: A Review.Medicina (Kaunas, Lithuania) · 2026Review
- Towards Automated Spine Fracture Detection on Whole-Body CT of Polytraumatized Patients.Journal of imaging · 2026Article
- Comparison of multistage and single-stage framework for automated landmark localization and radiologic measurement: a case of the C1-2 complex on cervical spine lateral radiographs.BMC medical imaging · 2026Article
- Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies.Journal of imaging informatics in medicine · 2026Article
- Intelligent Attention-Driven Deep Learning for Hip Disease Diagnosis: Fusing Multimodal Imaging and Clinical Text for Enhanced Precision and Early Detection.Medicina (Kaunas, Lithuania) · 2026Article
- Deep learning models to map osteocyte networks from confocal microscopy can successfully distinguish between young and aged bone.PLoS computational biology · 2026Article
- Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance.Journal of imaging informatics in medicine · 2025Article
- Modeling of Severity Classification Algorithm Using Abdominal Aortic Aneurysm Computed Tomography Image Segmentation Based on U-Net with Improved Noise Reduction Performance.Sensors (Basel, Switzerland) · 2025Article
- Current State of Evidence for Use of MRI in LI-RADS.Journal of magnetic resonance imaging : JMRI · 2025Review
- Construction of prognostic scoring model for ovarian cancer based on deep learning algorithm.Discover oncology · 2025Article
- Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.Journal of imaging informatics in medicine · 2025Article
- Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning.Insights into imaging · 2025Article
- Advance in peptide-based drug development: delivery platforms, therapeutics and vaccines.Signal transduction and targeted therapy · 2025Review
- A deep learning aided bone marrow segmentation of quantitative fat MRI for myelofibrosis patients.Frontiers in oncology · 2025Article
- End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.Neuroradiology · 2025Article
- tUbeNet: a generalizable deep learning tool for 3D vessel segmentation.Biology methods & protocols · 2025Article
- Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.Radiology. Artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven approaches have great potential to shape future practices in radiology. The most straightforward strategy to obtain clinically accurate models is to use large, well-curated and annotated datasets. However, patient privacy constraints, tedious annotation processes, and the limited availability of radiologists pose challenges to building such datasets. This review details model training strategies in scenarios with limited data, insufficiently labeled data, and/or limited expert resources. This review discusses strategies to enlarge the data sample, decrease the time burden of manual supervised labeling, adjust the neural network architecture to improve model performance, apply semisupervised approaches, and leverage efficiencies from pretrained models.
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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 Socratic graph.