# MAM-E: Mammographic synthetic image generation with diffusion models

arXiv: https://arxiv.org/abs/2311.09822 · HTML: https://mlsanity.com/paper/2311.09822/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 23 of its 36 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 23 of 23)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Brain Imaging Generation with Latent Diffusion Models](https://mlsanity.com/paper/2209.07162/) (2022)
- [CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders](https://mlsanity.com/paper/2106.14843/) (2021)
- [Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild](https://mlsanity.com/paper/1906.02569/) (2019)
- [RoentGen: Vision-Language Foundation Model for Chest X-ray Generation](https://mlsanity.com/paper/2211.12737/) (2022)
- [Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains](https://mlsanity.com/paper/2210.04133/) (2022)
- [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022)
- [Generative AI for Medical Imaging: extending the MONAI Framework](https://mlsanity.com/paper/2307.15208/) (2023)
- [OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data](https://mlsanity.com/paper/2004.04742/) (2020)
- [Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report](https://mlsanity.com/paper/2210.12113/) (2022)
- [A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms](https://mlsanity.com/paper/2112.04490/) (2021)
- [High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks](https://mlsanity.com/paper/1807.03401/) (2018)
- [DCGANs for Realistic Breast Mass Augmentation in X-ray Mammography](https://mlsanity.com/paper/1909.02062/) (2019)

## Built on by (most cited first, 11 of 11)

- [Improved Generation of Synthetic Imaging Data Using Feature-Aligned Diffusion](https://mlsanity.com/paper/2410.00731/) (2024)
- [Joint Holistic and Lesion Controllable Mammogram Synthesis via Gated Conditional Diffusion Model](https://mlsanity.com/paper/2507.19201/) (2025)
- [MammoRGB: Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2511.22759/) (2025)
- [Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Model](https://mlsanity.com/paper/2407.14326/) (2024)
- [Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models](https://mlsanity.com/paper/2504.10883/) (2025)
- [SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions](https://mlsanity.com/paper/2507.19970/) (2025)
- [Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2604.05110/) (2026)
- [BAC-JEPA: Label-Efficient Breast Arterial Calcification Segmentation via Synthetic Mammography-Guided Supervision](https://mlsanity.com/paper/2606.22089/) (2026)
- [MammoFlow: Multiview Mammogram Synthesis with Anatomically Consistent Flow Matching](https://mlsanity.com/paper/2606.28537/) (2026)
- [Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions](https://mlsanity.com/paper/2607.21904/) (2026)
- [What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization](https://mlsanity.com/paper/2609.24691/) (2026)

## Abstract (arXiv)

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.