Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

📰 ArXiv cs.AI

Denoising diffusion probabilistic models can synthesize simultaneous dual-view mammograms, addressing the issue of incomplete paired views in breast cancer screening datasets

advanced Published 8 Apr 2026

Action Steps

Utilize denoising diffusion probabilistic models to generate CC and MLO views
Train the model on available datasets to learn cross-view consistency
Evaluate the generated views for diagnostic accuracy and consistency with real images
Integrate the synthesized views into existing breast cancer screening algorithms

Who Needs to Know This

This research benefits data scientists and AI engineers working on medical imaging projects, as it provides a novel approach to generating complementary views for diagnosis

Key Insight

💡 Denoising diffusion probabilistic models can generate high-quality, complementary mammogram views, improving breast cancer screening dataset completeness

Key Takeaways

Denoising diffusion probabilistic models can synthesize simultaneous dual-view mammograms, addressing the issue of incomplete paired views in breast cancer screening datasets

Full Article

Title: Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

Abstract:
arXiv:2604.05110v1 Announce Type: cross 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 singl

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