Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

TL;DR

This paper introduces a novel diffusion model that synthesizes paired mammogram views simultaneously, enhancing dataset completeness and supporting advanced AI applications in breast imaging.

Contribution

A three-channel denoising diffusion probabilistic model that generates coherent dual-view mammograms with difference-guided learning, improving cross-view consistency.

Findings

The model preserves global breast structure across views.

Synthetic pairs resemble real mammograms in distribution and geometry.

Difference encoding enhances cross-view anatomical coherence.

Abstract

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.