mlsanityData to 7 Oct 2026

A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images

arXiv 2403.04612 ↗First posted 7 Mar 2024eess.IV cs.AI cs.CVcited by 4

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Currently, medical image domain translation operations show a high demand from researchers and clinicians. Amongst other capabilities, this task allows the generation of new medical images with sufficiently high image quality, making them clinically relevant. Deep Learning (DL) architectures, most specifically deep generative models, are widely used to generate and translate images from one domain to another. The proposed framework relies on an adversarial Denoising Diffusion Model (DDM) to synthesize echocardiography images and perform domain translation. Contrary to Generative Adversarial Networks (GANs), DDMs are able to generate high quality image samples with a large diversity. If a DDM is combined with a GAN, this ability to generate new data is completed at an even faster sampling time. In this work we trained an adversarial DDM combined with a GAN to learn the reverse denoising process, relying on a guide image, making sure relevant anatomical structures of each echocardiography image were kept and represented on the generated image samples. For several domain translation operations, the results verified that such generative model was able to synthesize high quality image samples: MSE: 11.50 +/- 3.69, PSNR (dB): 30.48 +/- 0.09, SSIM: 0.47 +/- 0.03. The proposed method showed high generalization ability, introducing a framework to create echocardiography images suitable to be used for clinical research purposes.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 24 of its 37 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

24 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Models17 Jul

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  7. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  8. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  9. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  10. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 670
  11. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 515
  12. 2019Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiographycited by 243
11 more
  1. 2017Image to Image Translation for Domain Adaptationcited by 171
  2. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 131
  3. 2021An Introduction to Deep Generative Modelingcited by 104
  4. 2022Diffusion Deformable Model for 4D Temporal Medical Image Generationcited by 65
  5. 2021GANs for Medical Image Synthesis: An Empirical Studycited by 64
  6. 2019DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesiscited by 36
  7. 2020Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluationcited by 36
  8. 2020Memory-efficient GAN-based Domain Translation of High Resolution 3D Medical Imagescited by 14
  9. 2020Cross-Domain Medical Image Translation by Shared Latent Gaussian Mixture Modelcited by 12
  10. 2020STAN-CT: Standardizing CT Image using Generative Adversarial Networkcited by 11
  11. 20204D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Modelcited by 7

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2025Generative augmentations for improved cardiac ultrasound segmentation using diffusion modelscited by 3
  2. 2024Cardiac valve event timing in echocardiography using deep learning and triplane recordingscited by 1
  3. 2024Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkerscited by 1
  4. 2025A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosiscited by 1

Similar, not cited

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  1. 2024Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms
  2. 2024Self-Consistent Recursive Diffusion Bridge for Medical Image Translation
  3. 2025Regression is all you need for medical image translation
  4. 2024Cross-conditioned Diffusion Model for Medical Image to Image Translation
  5. 2024Deep Generative Models for 3D Medical Image Synthesis
  6. 2023Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
  7. 2024Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
  8. 2024Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation