用Mamba+图结构+傅里叶优化，精准预测阿尔茨海默病影像进展
MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction
- 结合Mamba长程建模与图引导控制，捕捉病变时空演化
- 在ADNI数据集上显著提升轨迹预测精度与解剖保真度
- 适合做神经退行性疾病个性化预后与临床辅助决策
精准医学中的疾病进展建模需同时捕捉复杂的时空动态并保持解剖结构一致性。现有方法常难以处理纵向依赖关系和进行结构性一致建模。为此，我们提出MambaControl，一种将选择性状态空间模型与扩散过程结合的框架，用于高保真医学图像轨迹预测。为更准确地表征随时间演变的细微结构变化并维持解剖一致性，MambaControl融合基于Mamba的长距离建模与图引导的解剖控制，以有效刻画解剖关联。此外，引入傅里叶增强的谱图表示，以捕获空间连贯性和多尺度细节，使MambaControl在阿尔茨海默病预测任务中达到当前最优性能。定量及区域评估表明，其在进展预测质量与解剖保真度方面均有显著提升，展现出个性化预后与临床决策支持的潜力。
原文摘要 · Abstract (English)
Modelling disease progression in precision medicine requires capturing complex spatio-temporal dynamics while preserving anatomical integrity. Existing methods often struggle with longitudinal dependencies and structural consistency in progressive disorders. To address these limitations, we introduce MambaControl, a novel framework that integrates selective state-space modelling with diffusion processes for high-fidelity prediction of medical image trajectories. To better capture subtle structural changes over time while maintaining anatomical consistency, MambaControl combines Mamba-based long-range modelling with graph-guided anatomical control to more effectively represent anatomical correlations. Furthermore, we introduce Fourier-enhanced spectral graph representations to capture spatial coherence and multiscale detail, enabling MambaControl to achieve state-of-the-art performance in Alzheimer's disease prediction. Quantitative and regional evaluations demonstrate improved progression prediction quality and anatomical fidelity, highlighting its potential for personalised prognosis and clinical decision support.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。