mlsanityData to 7 Oct 2026

Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models

arXiv 2410.23835 ↗First posted 31 Oct 2024eess.IV cs.AI cs.CVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual magnetic resonance (MR) images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for data augmentation can improve segmentation accuracy, particularly in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https: //github.com/pedromorao/Counterfactual-MRI-Data-Augmentation

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 11 of its 12 references that are in the map. First posted on arXiv 31 Oct 2024.

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11 of 12 references.

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  1. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep

Other references · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2022Classifier-Free Diffusion Guidancecited by 5,717
  7. 2019Invariant Risk Minimizationcited by 1,832
  8. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  9. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  10. 2023Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Imagescited by 7

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  3. 2024Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs
  4. 2025Generating healthy counterfactuals with denoising diffusion bridge models
  5. 2025Integrating Anatomical Priors into a Causal Diffusion Model
  6. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  7. 2024MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI
  8. 2023Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images