MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction
This work addresses the challenge of modeling disease trajectories in precision medicine for clinical decision support, representing an incremental advancement over existing methods.
The paper tackles the problem of predicting disease progression in medical images by developing MambaControl, a framework that integrates selective state-space modeling with diffusion processes to capture complex spatio-temporal dynamics while preserving anatomical integrity, achieving state-of-the-art performance in Alzheimer's disease prediction with improved progression prediction quality and anatomical fidelity.
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.