mlsanityData to 7 Oct 2026

Synthetic Data in Radiological Imaging: Current State and Future Outlook

arXiv 2407.01561 ↗First posted 8 May 2024eess.IV cs.AI cs.CVcited by 7

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Abstract (arXiv)

A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 44 of its 117 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

44 of 117 references.

References · 44

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
  5. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  6. 2023Extracting Training Data from Diffusion Modelscited by 723
  7. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  8. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 351
  9. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  10. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  11. 2023Stable Bias: Analyzing Societal Representations in Diffusion Modelscited by 225
  12. 2021How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Modelscited by 193
32 more
  1. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  2. 2018GAN Augmentation: Augmenting Training Data using Generative Adversarial Networkscited by 107
  3. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  4. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  5. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  6. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  7. 2020On hallucinations in tomographic image reconstructioncited by 66
  8. 2017Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Trainingcited by 57
  9. 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
  10. 2021SinGAN-Seg: Synthetic training data generation for medical image segmentationcited by 43
  11. 2020Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluationcited by 36
  12. 2018A Gentle Introduction to Deep Learning in Medical Image Processingcited by 34
  13. 2023Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentationcited by 33
  14. 2018Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detectioncited by 29
  15. 2020COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Networkcited by 29
  16. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Modelscited by 27
  17. 2020Pseudo-healthy synthesis with pathology disentanglement and adversarial learningcited by 19
  18. 2023Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesiscited by 19
  19. 2019Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NETcited by 13
  20. 2020Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translationcited by 13
  21. 2021Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networkscited by 12
  22. 2022Assessing the ability of generative adversarial networks to learn canonical medical image statisticscited by 11
  23. 2018Generating Synthetic X-ray Images of a Person from the Surface Geometrycited by 9
  24. 2020Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstructioncited by 9
  25. 2023Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and dosescited by 8
  26. 2017Precision Learning: Towards Use of Known Operators in Neural Networkscited by 7
  27. 2023The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohortscited by 6
  28. 2022SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experimentscited by 5
  29. 2021Synthetic magnetic resonance images for domain adaptation: Application to fetal brain tissue segmentationcited by 4
  30. 2021Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Imagescited by 3
  31. 2021A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANscited by 1
  32. 2022Free-form Lesion Synthesis Using a Partial Convolution Generative Adversarial Network for Enhanced Deep Learning Liver Tumor Segmentationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2024Scorecards for Synthetic Medical Data Evaluation and Reportingcited by 4
  2. 2025DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understandingcited by 2
  3. 2025Performance Assessment Strategies for Language Model Applications in Healthcarecited by 0
  4. 2025Knowledge-based anomaly detection for identifying network-induced shape artifactscited by 0
  5. 2025Upstream Probabilistic Meta-Imputation for Multimodal Pediatric Pancreatitis Classificationcited by 0
  6. 2026Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstructioncited by 0
  7. 2026The Slop Paradox: How Synthetic Standardization Erodes Clinical Uncertainty and Cross-Modal Alignment in AI-Rewritten Radiology Reportscited by 0