mlsanityData to 7 Oct 2026

Denoising Diffusion Medical Models

arXiv 2304.09383 ↗First posted 19 Apr 2023eess.IV cs.CV cs.GRcited by 6

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentations on a small number of annotated datasets as well as other massive unlabeled datasets with no supervision. Radiograph/segmentation pairs are generated jointly by the DDMM sampling process in probabilistic mode. As a result, a vanilla UNet that uses this data augmentation for segmentation task outperforms other similarly data-centric approaches.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 11 of its 23 references that are in the map. First posted on arXiv 19 Apr 2023.

Builds on

11 of 23 references.

References · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2018Demystifying MMD GANscited by 1,268
  5. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 368
  6. 2020VinDr-CXR: An open dataset of chest X-rays with radiologist's annotationscited by 236
  7. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 146
  8. 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
  9. 2021Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representationscited by 31
  10. 2020XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priorscited by 8
  11. 2022Neural Radiance Projectioncited by 2

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 2.

6 papers.

  1. 2024Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challengescited by 24
  2. 2025Metrics that matter: Evaluating image quality metrics for medical image generationcited by 12
  3. 2024On the Asymptotic Mean Square Error Optimality of Diffusion Modelscited by 10
  4. 2023Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial contextcited by 7
  5. 2024Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Modelscited by 2
  6. 2025MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imagingcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2022Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation
  2. 2023Pre-Training with Diffusion models for Dental Radiography segmentation
  3. 2024DDPM based X-ray Image Synthesizer
  4. 2023Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation
  5. 2024Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
  6. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
  7. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
  8. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Survey