Ablation study of RGB encoding strategies for diffusion-based dual-view mammogram synthesis

The availability of large multi-view mammographic datasets is limited by privacy concerns and acquisition variability, restricting the development of robust artificial intelligence models. This study investigates the impact of RGB encoding strategies on diffusion-based simultaneous dual-view mammogram synthesis. A Denoising Diffusion Probabilistic Model was fine-tuned using RGB representations that encode craniocaudal and mediolateral oblique views with different third-channel formulations. Three encoding strategies were evaluated using image quality metrics, distribution-based comparisons, and anatomical consistency measurements. Results show that zero-channel and difference representations achieved comparable performance, while sum-based encoding provided lower performance. These findings highlight the importance of input representation design for generating consistent synthetic multi-view mammograms.