Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high quality of the generated images and their relatively less complex training process compared with Generative Adversarial Networks. Still, the implementation of such models in the medical domain remains at early stages. In this work, we propose exploring the use of diffusion models for the generation of high quality full-field digital mammograms using state-of-the-art conditional diffusion pipelines. Additionally, we propose using stable diffusion models for the inpainting of synthetic lesions on healthy mammograms. We introduce MAM-E, a pipeline of generative models for high quality mammography synthesis controlled by a text prompt and capable of generating synthetic lesions on specific regions of the breast. Finally, we provide quantitative and qualitative assessment of the generated images and easy-to-use graphical user interfaces for mammography synthesis.

翻译：生成模型作为一种替代数据增强技术，用于缓解医学影像领域面临的数据稀缺问题。扩散模型因其创新的生成方法、生成图像的高质量以及相比生成对抗网络相对更简单的训练过程而受到特别关注。然而，此类模型在医学领域的应用仍处于早期阶段。在本工作中，我们提出探索利用扩散模型，通过最先进的条件扩散管线生成高质量的全视野数字乳腺X线影像。此外，我们提出使用稳定扩散模型对健康乳腺X线影像进行合成病灶的图像修复。我们引入了MAM-E，这是一套由文本提示控制的高质量乳腺X线成像生成模型管线，能够在乳腺特定区域生成合成病灶。最后，我们对生成的图像进行了定量与定性评估，并提供了易于使用的乳腺X线影像合成图形用户界面。