论文标题
医学扩散：3D医疗图像生成的降解扩散概率模型
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
论文作者
论文摘要
计算机视觉的最新进展显示出图像生成的有希望的结果。尤其是扩散概率模型已经从文本输入中产生了逼真的图像，如DALL-E 2，成像和稳定扩散所证明的那样。但是，它们在医学中的使用（图像数据通常包含三维体积）尚未系统地评估。合成图像可能在保留人工智能的隐私中起着至关重要的作用，也可以用于增强小型数据集。在这里，我们表明，扩散概率模型可以合成高质量的医学成像数据，我们为磁共振图像（MRI）和计算机断层扫描（CT）图像显示。我们通过一项读者研究提供定量测量，并与两位医学专家一起评估了三类合成图像的质量：现实图像外观，解剖学的正确性和切片之间的一致性。此外，我们证明，当数据稀缺时，合成图像可以用于自我监督的预训练并改善乳房分割模型的性能（骰子得分0.91 vs. 0.95 vs. 0.95，而没有合成数据）。该代码可在GitHub上公开获取：https：//github.com/firasgit/medicaldiffusion。
Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy preserving artificial intelligence and can also be used to augment small datasets. Here we show that diffusion probabilistic models can synthesize high quality medical imaging data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography (CT) images. We provide quantitative measurements of their performance through a reader study with two medical experts who rated the quality of the synthesized images in three categories: Realistic image appearance, anatomical correctness and consistency between slices. Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0.91 vs. 0.95 without vs. with synthetic data). The code is publicly available on GitHub: https://github.com/FirasGit/medicaldiffusion.
