Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models

Pedro Morão, Joao Santinha, Yasna Forghani, Nuno Loução, Pedro Gouveia +1 more
10/31/2024
eess.IVcs.AIcs.CV

No official code found. Best implementation: pedromorao/Counterfactual-MRI-Data-Augmentation (3 stars, MIT, last commit 2024-09-08).

Abstract

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

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Code Implementations(1)

pedromorao/Counterfactual-MRI-Data-AugmentationCommunity100%
MIT
30Python, Jupyter NotebookAug 28, 20242 years agoMIT

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Cite this paper

@article{moro2024counterfactual,
  title  = {Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models},
  author = {Pedro Morão and Joao Santinha and Yasna Forghani and Nuno Loução and Pedro Gouveia and Mario A. T. Figueiredo},
  year   = {2024},
  eprint = {2410.23835},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.23835v1}
}

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