# Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

arXiv: https://arxiv.org/abs/2604.05110 · HTML: https://mlsanity.com/paper/2604.05110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 6 Apr 2026.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems](https://mlsanity.com/paper/2211.12343/) (2022)
- [MAM-E: Mammographic synthetic image generation with diffusion models](https://mlsanity.com/paper/2311.09822/) (2023)
- [Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classification](https://mlsanity.com/paper/2306.10676/) (2023)
- [Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures](https://mlsanity.com/paper/2305.19937/) (2023)
- [Ensemble of radiomics and ConvNeXt for breast cancer diagnosis](https://mlsanity.com/paper/2601.05373/) (2026)

## Abstract (arXiv)

Breast cancer screening relies heavily on mammography, where the craniocaudal (CC) and mediolateral oblique (MLO) views provide complementary information for diagnosis. However, many datasets lack complete paired views, limiting the development of algorithms that depend on cross-view consistency. To address this gap, we propose a three-channel denoising diffusion probabilistic model capable of simultaneously generating CC and MLO views of a single breast. In this configuration, the two mammographic views are stored in separate channels, while a third channel encodes their absolute difference to guide the model toward learning coherent anatomical relationships between projections. A pretrained DDPM from Hugging Face was fine-tuned on a private screening dataset and used to synthesize dual-view pairs. Evaluation included geometric consistency via automated breast mask segmentation and distributional comparison with real images, along with qualitative inspection of cross-view alignment. The results show that the difference-based encoding helps preserve the global breast structure across views, producing synthetic CC-MLO pairs that resemble real acquisitions. This work demonstrates the feasibility of simultaneous dual-view mammogram synthesis using a difference-guided DDPM, highlighting its potential for dataset augmentation and future cross-view-aware AI applications in breast imaging.