MAM-E: Mammographic synthetic image generation with diffusion models
Ricardo Montoya-del-Angel Karla Sam-Millan Joan C Vilanova Robert Martí
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.