用扩散模型生成假想MRI图像，提升模型泛化能力
Counterfactual MRI Data Augmentation using Conditional Denoising Diffusion Generative Models
- 用条件去噪扩散模型生成不同扫描参数的假想MRI
- 在分布外场景下分割准确率提升，增强模型鲁棒性
- 适合需要跨设备泛化的医学影像深度学习研究者
医学影像中的深度学习模型因成像参数差异面临泛化与鲁棒性挑战。本文提出一种基于条件去噪扩散生成模型（cDDGM）的新方法，生成模拟不同成像参数但不改变患者解剖结构的反事实MR图像。实验表明，利用这些反事实图像进行数据增强可显著提升分割精度，尤其在分布外设置下，有效增强模型在多样化成像条件下的泛化能力。该方法对解决医学影像中的域偏移和协变量偏移具有潜力。代码已公开于 https://github.com/pedromorao/Counterfactual-MRI-Data-Augmentation。
原文摘要 · Abstract (English)
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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