Domain-Generalized Mamba Networks for Multi-Center Medical Image Classification
Keywords:
domain generalization, Mamba networks, multi-center medical imaging, federated learning, clinical AI governance, state-space modelsAbstract
Multi-center medical image classification is a central task in clinical artificial intelligence, yet its practical adoption remains constrained by severe domain shift across acquisition equipment, reconstruction pipelines, and patient populations. Selective state-space sequence models, particularly Mamba-style architectures, offer linear-complexity long-range modeling and create new opportunities for domain-generalized diagnostic pipelines. This paper presents a system-level analysis of domain-generalized Mamba networks for multi-center medical image classification, treating the model not as an isolated algorithm but as a component within a broader socio-technical infrastructure. The discussion examines architectural trade-offs between convolutional, attention-based, and state-space designs, with emphasis on memory efficiency, input resolution, and domain invariance. It further addresses multi-center data governance, federated readiness, deployment architecture, drift monitoring, and regulatory alignment. The paper argues that the value of domain-generalized state-space models depends on more than benchmark accuracy; it requires deliberate integration with data provenance systems, computational constraints, fairness audits, and clinical safety governance. Robustness under distribution shift, sustainability across hardware generations, and contestability of model decisions are positioned as first-order design requirements. The analysis synthesizes evidence from medical image analysis, distributed learning, fairness research, and clinical model reporting to provide a forward-looking research agenda for decentralized and domain-generalized medical imaging systems.
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