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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.09965 |
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| _version_ | 1866908364399706112 |
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| author | Yang, Hao Tan, Tao Tan, Shuai Yang, Weiqin Cai, Kunyan Chen, Calvin Sun, Yue |
| author_facet | Yang, Hao Tan, Tao Tan, Shuai Yang, Weiqin Cai, Kunyan Chen, Calvin Sun, Yue |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09965 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction Yang, Hao Tan, Tao Tan, Shuai Yang, Weiqin Cai, Kunyan Chen, Calvin Sun, Yue Computer Vision and Pattern Recognition 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. |
| title | MambaControl: Anatomy Graph-Enhanced Mamba ControlNet with Fourier Refinement for Diffusion-Based Disease Trajectory Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.09965 |