Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models (2410.23835v1)
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
- Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019.
- A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 dce-mri features. British journal of cancer, 119(4):508–516, 2018.
- Reverse engineering breast mris: Predicting acquisition parameters directly from images. In Medical Imaging with Deep Learning, pages 829–845. PMLR, 2024.
- Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
- Denoising diffusion probabilistic models, 2020. URL https://arxiv.org/abs/2006.11239.
- Classifier-free diffusion guidance, 2022. URL https://arxiv.org/abs/2207.12598.
- Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022. URL https://arxiv.org/abs/2108.01073.
- U-net: Convolutional networks for biomedical image segmentation. CoRR, abs/1505.04597, 2015.
- High-resolution image synthesis with latent diffusion models, 2022. URL https://arxiv.org/abs/2112.10752.
- Brain imaging generation with latent diffusion models, 2022. URL https://arxiv.org/abs/2209.07162.
- Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
- Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701, 2022.
- Generative ai for medical imaging: extending the monai framework. arXiv preprint arXiv:2307.15208, 2023.
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