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MAM-E: Mammographic Synthetic Image Generation with Diffusion Models.

Ricardo Montoya-Del-Angel1, Karla Sam-Millan1, Joan C Vilanova2

  • 1Computer Vision and Robotics Institute (ViCOROB), University of Girona, 17004 Girona, Spain.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study explores diffusion models for generating high-quality synthetic mammograms and mass-like lesions. The MAM-E pipeline offers controlled mammography synthesis, addressing data scarcity in medical imaging.

Keywords:
diffusion modelslesion inpaintingmammography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Data scarcity is a significant challenge in medical imaging research.
  • Generative models, particularly diffusion models, show promise for data augmentation.
  • Diffusion models offer high-quality image generation with less complex training than GANs.

Purpose of the Study:

  • To explore diffusion models for synthesizing high-quality, full-field digital mammograms.
  • To utilize stable diffusion models for inpainting synthetic mass-like lesions.
  • To introduce MAM-E, a text-prompt-controlled pipeline for mammography synthesis and lesion generation.

Main Methods:

  • Implementation of state-of-the-art conditional diffusion pipelines for mammogram generation.
  • Application of stable diffusion models for synthetic lesion inpainting.
  • Development of the MAM-E pipeline with text-prompt control and region specification.
  • Quantitative and qualitative assessment of generated images.

Main Results:

  • Successful generation of high-quality synthetic full-field digital mammograms.
  • Effective inpainting of synthetic mass-like lesions on healthy mammograms using stable diffusion.
  • Demonstration of MAM-E's capability for controlled mammography synthesis and lesion generation.
  • Provision of assessment metrics and user-friendly graphical interfaces.

Conclusions:

  • Diffusion models are a viable tool for medical image synthesis and data augmentation.
  • The MAM-E pipeline provides a controllable method for generating realistic mammograms with synthetic lesions.
  • Further research and implementation of these generative models can advance medical imaging analysis and training.