MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction

• URL: http://arxiv.org/abs/2505.09965v1
• Date: Thu, 15 May 2025 04:59:02 GMT
• Title: MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction
• Authors: Hao Yang, Tao Tan, Shuai Tan, Weiqin Yang, Kunyan Cai, Calvin Chen, Yue Sun,
• Abstract summary: We introduce MambaControl, a framework that integrates selective state-space modelling with diffusion processes for high-fidelity prediction of medical image trajectories.<n>MambaControl achieves state-of-the-art performance in Alzheimer's disease prediction.
• Score: 12.11009754294397
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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