Graph-Mamba Networks for Integrating Anatomical Relationships in Medical Image Analysis
Keywords:
Graph-Mamba networks, medical image analysis, anatomical graphs, state-space models, clinical deployment, robustness, fairness, governanceAbstract
Medical image analysis increasingly requires computational systems that can reason over spatially distributed anatomical structures rather than isolated pixels or patches. Graph-Mambanetworks integrate the relational expressiveness of graph neural networks with the linear-time sequence modeling capabilities of state-space models. This paper presents a system-level examination of Graph-Mamba architectures for medical image analysis, with particular attention to how anatomical relationships are encoded, propagated, and constrained across heterogeneous imaging modalities. Rather than focusing narrowly on benchmark performance, the discussion interrogates structural trade-offs between local anatomical fidelity and global contextual reasoning, the implications of selective state-space propagation for long-range dependencies, and the infrastructural demands of deploying graph-structured state-space systems in clinical environments. The paper further explores governance mechanisms for safety assurance, robustness against distributional shift, fairness across patient populations, and sustainability concerns arising from model scale and data movement. A cross-domain comparative lens is adopted to contrast Graph-Mamba designs with convolutional, transformer, and attention-based graph systems, highlighting where hybrid relational-state-space models offer distinct advantages for organ segmentation, disease staging, and longitudinal anatomical modeling. The analysis identifies key architectural decision points including graph construction granularity, bidirectional state propagation, memory gating, and regularization of anatomical priors. It argues that the value of Graph-Mamba in medical imaging resides not only in representational capacity but also in its potential to support more interpretable, resource-aware, and governable learning systems when coupled with explicit anatomical constraints. Throughout, emphasis is placed on the socio-technical embedding of such models in clinical workflows, diagnostic accountability, and the broader regulatory landscape. The paper concludes with forward-looking perspectives on federated learning, uncertainty quantification, and policy-aligned evaluation frameworks for next-generation medical image analysis infrastructures.
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