1. Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
- Author
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Morão, Pedro, Santinha, Joao, Forghani, Yasna, Loução, Nuno, Gouveia, Pedro, and Figueiredo, Mario A. T.
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - 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
- Published
- 2024