mlsanityData to 7 Oct 2026

Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models

arXiv 2211.00902 ↗First posted 2 Nov 2022eess.IV cs.AI cs.CVcited by 27

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Generative models are becoming popular for the synthesis of medical images. Recently, neural diffusion models have demonstrated the potential to generate photo-realistic images of objects. However, their potential to generate medical images is not explored yet. In this work, we explore the possibilities of synthesis of medical images using neural diffusion models. First, we use a pre-trained DALLE2 model to generate lungs X-Ray and CT images from an input text prompt. Second, we train a stable diffusion model with 3165 X-Ray images and generate synthetic images. We evaluate the synthetic image data through a qualitative analysis where two independent radiologists label randomly chosen samples from the generated data as real, fake, or unsure. Results demonstrate that images generated with the diffusion model can translate characteristics that are otherwise very specific to certain medical conditions in chest X-Ray or CT images. Careful tuning of the model can be very promising. To the best of our knowledge, this is the first attempt to generate lungs X-Ray and CT images using neural diffusion models. This work aims to introduce a new dimension in artificial intelligence for medical imaging. Given that this is a new topic, the paper will serve as an introduction and motivation for the research community to explore the potential of diffusion models for medical image synthesis. We have released the synthetic images on https://www.kaggle.com/datasets/hazrat/awesomelungs.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 10 of its 16 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

10 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep

Other references · 9

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  5. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  6. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200
  7. 2022CARD: Classification and Regression Diffusion Modelscited by 102
  8. 2020COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Networkcited by 29
  9. 2020Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networkscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 10; 2024: 9; 2025: 6; 2026: 2.

27 papers.

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  1. 2023Extracting Training Data from Diffusion Modelscited by 723
  2. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  3. 2023Dream the Impossible: Outlier Imagination with Diffusion Modelscited by 77
  4. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  5. 2023Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Imagescited by 45
  6. 2023Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesiscited by 32
  7. 2023Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray imagescited by 23
  8. 2024Differentially Private Fine-Tuning of Diffusion Modelscited by 18
  9. 2023Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedbackcited by 14
  10. 2024Elucidating Optimal Reward-Diversity Tradeoffs in Text-to-Image Diffusion Modelscited by 13
  11. 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
  12. 2025Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedbackcited by 9
15 more
  1. 2023Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generationcited by 7
  2. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7
  3. 2023Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass Segmentationcited by 5
  4. 2024DP-TLDM: Differentially Private Tabular Latent Diffusion Modelcited by 5
  5. 2024The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methodscited by 4
  6. 2024MEDDAP: Medical Dataset Enhancement via Diversified Augmentation Pipelinecited by 2
  7. 2025Causal Disentanglement for Robust Long-tail Medical Image Generationcited by 2
  8. 2024An Early Investigation into the Utility of Multimodal Large Language Models in Medical Imagingcited by 1
  9. 2025ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesiscited by 1
  10. 2026CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesiscited by 1
  11. 2024Exploring Foundation Models for Synthetic Medical Imaging: A Study on Chest X-Rays and Fine-Tuning Techniquescited by 0
  12. 2025Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion modelscited by 0
  13. 2025Parameter-Efficient Fine-Tuning of 3D DDPM for MRI Image Generation Using Tensor Networkscited by 0
  14. 2025AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representationscited by 0
  15. 2026Federated Balanced Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  2. 2024Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis
  3. 2024Diffusion-Based Approaches in Medical Image Generation and Analysis
  4. 2025Perceptual Evaluation of GANs and Diffusion Models for Generating X-rays
  5. 2025Lung-DDPM+: Efficient Thoracic CT Image Synthesis using Diffusion Probabilistic Model
  6. 2025Synthetic Lung X-ray Generation through Cross-Attention and Affinity Transformation
  7. 2024XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model
  8. 2024Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation