ReviewRadiology. Artificial intelligence2021
Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging.
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 15 papers.
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.
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Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- RadiolGAN: Multicenter Feasibility Study of Synthetic CT From 3D Ultra-Short Echo Time MRI for Enhanced Pulmonary Radiologic Sign Visualization.Journal of magnetic resonance imaging : JMRI · 2026Article
- MedNet-FS: a few-shot learning framework for 3D MRI-based knee injury classification.Scientific reports · 2026Article
- Swin Transformer-based intelligent fracture classification and radiographic assessment to support clinical decision-making.BMC medical informatics and decision making · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Diagnosing Ankylosing Spondylitis via Architecture-Modified ResNet and Combined Conventional Magnetic Resonance Imagery.Journal of imaging informatics in medicine · 2025Article
- Exploration of 3D Few-Shot Learning Techniques for Classification of Knee Joint Injuries on MR Images.Diagnostics (Basel, Switzerland) · 2025Article
- Structure-property relationships in fibrous meniscal tissue through image-based augmentation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Mutual Information Guided Diffusion for Zero-Shot Cross-Modality Medical Image Translation.IEEE transactions on medical imaging · 2024Article
- Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery.Scientific reports · 2024Article
- Imaging-based deep learning in kidney diseases: recent progress and future prospects.Insights into imaging · 2024Review
- Educational Overview of the Concept and Application of Computer Vision in Arthroplasty.The Journal of arthroplasty · 2023Review
- Photoacoustic image synthesis with generative adversarial networks.Photoacoustics · 2022Article
- Multi-Contrast MRI Segmentation Trained on Synthetic Images.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey.Eye and vision (London, England) · 2022Review
- Utilizing conditional generative adversarial network to generate head MRA based on nonvascular sequences: comparative study of single-modality and multi-modality methods.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, deep learning techniques have been applied in musculoskeletal radiology to increase the diagnostic potential of acquired images. Generative adversarial networks (GANs), which are deep neural networks that can generate or transform images, have the potential to aid in faster imaging by generating images with a high level of realism across multiple contrast and modalities from existing imaging protocols. This review introduces the key architectures of GANs as well as their technical background and challenges. Key research trends are highlighted, including:
Identifiers
What OpenQuestion 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 OpenQuestion graph.