Computationally Efficient Mamba Models for Large-Scale Medical Image Retrieval
Keywords:
Medical image retrieval, Mamba, selective state space models, computational efficiency, large-scale infrastructure, fairness, governanceAbstract
Large-scale medical image retrieval systems must reconcile high diagnostic relevance, low inference latency, memory efficiency, and robust governance across heterogeneous clinical settings. This article analyzes the integration of Mamba, a selective state space sequence model, into retrieval pipelines for medical image repositories. The discussion adopts a system-level orientation rather than focusing narrowly on benchmark accuracy. It examines architectural decisions, computational trade-offs, deployment infrastructure, fairness, sustainability, and clinical accountability. Mamba architectures replace quadratic attention mechanisms with selective scan operations that scale approximately linearly with sequence length, making them attractive for high-resolution radiology, pathology, and multi-view imaging. However, moving from research prototypes to production retrieval systems raises structural issues. Patch tokenization order, bidirectional state propagation, hybrid attention-state space designs, and embedding normalization directly influence retrieval precision and failure modes. At the infrastructure layer, the paper considers vector database integration, sharding, query planning, caching, and fallback strategies in hospital networks. Governance and fairness concerns arise from dataset composition, minority anatomical presentation, disease prevalence imbalances, and the difficulty of explaining similarity decisions. The article further discusses energy use, model refresh cycles, and procurement policies. By synthesizing architecture, infrastructure, and policy perspectives, the paper offers a cross-disciplinary framework for building computationally efficient Mamba-based medical image retrieval systems that are not only accurate but also operationally sustainable and ethically defensible. Efficiency gains must be coupled with transparent evaluation, resource-aware deployment, and inclusive governance mechanisms to avoid amplifying existing clinical inequities.
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