mlsanityData to 7 Oct 2026

Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research

arXiv 2311.09402 ↗First posted 15 Nov 2023cs.CV cs.LGcited by 18

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research. The study employed DDPMs to create synthetic CXRs conditioned on demographic and pathological characteristics from the CheXpert dataset. These synthetic images were used to supplement training datasets for pathology classifiers, with the aim of improving their performance. The evaluation involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and various experiments, including supplementing real data with synthetic data, training with purely synthetic data, and mixing synthetic data with external datasets. Performance was assessed using the area under the receiver operating curve (AUROC). Adding synthetic data to real datasets resulted in a notable increase in AUROC values (up to 0.02 in internal and external test sets with 1000% supplementation, p-value less than 0.01 in all instances). When classifiers were trained exclusively on synthetic data, they achieved performance levels comparable to those trained on real data with 200%-300% data supplementation. The combination of real and synthetic data from different sources demonstrated enhanced model generalizability, increasing model AUROC from 0.76 to 0.80 on the internal test set (p-value less than 0.01). In conclusion, synthetic data supplementation significantly improves the performance and generalizability of pathology classifiers in medical imaging.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 33 of its 63 references that are in the map. First posted on arXiv 15 Nov 2023.

Builds on

33 of 63 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis20 Mar
  2. 2022Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems2 Nov
  3. 2022Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report21 Oct

Other references · 30

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  6. 2022Classifier-Free Diffusion Guidancecited by 5,717
  7. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  8. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  9. 2022A ConvNet for the 2020scited by 3,545
  10. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  11. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  12. 2023Extracting Training Data from Diffusion Modelscited by 723
18 more
  1. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  2. 2023Symbolic Discovery of Optimization Algorithmscited by 423
  3. 2023The Curse of Recursion: Training on Generated Data Makes Models Forgetcited by 343
  4. 2017Are GANs Created Equal? A Large-Scale Studycited by 267
  5. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  6. 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 155
  7. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  8. 2023Generative models improve fairness of medical classifiers under distribution shiftscited by 90
  9. 2022RoentGen: Vision-Language Foundation Model for Chest X-ray Generationcited by 89
  10. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  11. 2023Matryoshka Diffusion Modelscited by 70
  12. 2018Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classificationcited by 57
  13. 2022Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Studycited by 45
  14. 2023DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Modelscited by 41
  15. 2021Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Datacited by 38
  16. 2020CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Outputcited by 23
  17. 2020Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRIcited by 17
  18. 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

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 7; 2026: 7.

18 papers.

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  1. 2024MAISI: Medical AI for Synthetic Imagingcited by 86
  2. 2024Robust image representations with counterfactual contrastive learningcited by 12
  3. 2025Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Datacited by 7
  4. 2025Ultrasound Image Generation using Latent Diffusion Modelscited by 6
  5. 2024Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classificationcited by 4
  6. 2025Fair Foundation Models for Medical Image Analysis: Challenges and Perspectivescited by 3
  7. 2025FunnyNodules: A Customizable Medical Dataset Tailored for Evaluating Explainable AIcited by 2
  8. 2026Scaling Generative Foundation Models for Chest Radiography with Rectified Flow Transformerscited by 2
  9. 2024Generative AI in Medicinecited by 1
  10. 2026Cortex-Grounded Diffusion Models for Brain Image Generationcited by 1
  11. 2025RELICT: A Replica Detection Framework for Medical Image Generationcited by 0
  12. 2025A Statistical Approach for Synthetic EEG Data Generationcited by 0
6 more
  1. 2025A Reinforcement Learning Approach to Synthetic Data Generationcited by 0
  2. 2026A Calibrated Memorization Index (MI) for Detecting Training Data Leakage in Generative MRI Modelscited by 0
  3. 2026SALIENT: Frequency-Aware Paired Diffusion for Controllable Long-Tail CT Detectioncited by 0
  4. 2026Ordinal Diffusion Models for Color Fundus Imagescited by 0
  5. 2026Leak-Free Cross-Validated Stacking with Per-Architecture Calibration for Sand-Boil Segmentation in Earthen Leveescited by 0
  6. 2026To do(x) or not to do(x): Medical Image Counterfactuals for Dataset Augmentationcited by 0

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  1. 2024Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
  2. 2024Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniques
  3. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models
  4. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  5. 2024Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
  6. 2024Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records
  7. 2025Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation
  8. 2025A Generative Foundation Model for Chest Radiography