SAFE-Diff: Scale-Aware Attention and Feature-Dispersive Diffusion with Uncertainty Estimation for Contrast-Enhanced Breast MRI Synthesis

2026-05-25

AI Summary

Synthesizing high-fidelity contrast-enhanced breast MRI images remains challenging due to the complex texture of lesions and highly heterogeneous enhancement patterns, which hinder the accuracy and efficiency of breast cancer screening. This work proposes a novel diffusion-based generative model that innovatively integrates a scale-aware attention mechanism to capture multi-scale lesion characteristics, employs a feature-dispersed diffusion strategy to enhance textural diversity, and incorporates Bayesian uncertainty estimation to improve clinical reliability. The method significantly outperforms existing approaches in terms of image fidelity, lesion structural preservation, and consistency of enhancement patterns, thereby substantially enhancing the clinical utility of synthesized images.

Abstract

Synthesizing high fidelity contrast enhanced MRI is clinically valuable for safer and more efficient breast cancer screening, yet remains challenging due to complex lesion textures and heterogeneous enhancement patterns.

Problem

• contrast-enhanced MRI
• breast cancer screening
• image synthesis
• lesion texture
• enhancement heterogeneity

Innovation

• Scale-Aware Attention
• Feature-Dispersive Diffusion
• Uncertainty Estimation
• Contrast-Enhanced MRI Synthesis
• Breast Cancer Screening