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.

本研究提出了使用扩散模型生成高质量全场数字乳腺 X 线照片以及使用稳定扩散模型在健康乳腺照片上修复合成病变的方法，并引入了一个由文本提示控制的生成模型管道 MAM-E，能够在乳房特定区域生成合成病变，并提供了对生成图像的定量和定性评估以及易于使用的图形用户界面用于乳腺 X 线影像合成。