Attention-Mamba Fusion for Explainable Cardiovascular Disease Detection from Medical Images
Keywords:
attention mechanisms; state-space models; medical image classification; explainability; cardiovascular disease detection; data governanceAbstract
Recent advances in deep learning have enabled high-accuracy medical image classification, but cardiovascular disease detection remains difficult to translate into routine clinical practice because of interpretability gaps, heterogeneous imaging workflows, and governance barriers. This paper provides a system-level examination of attention-Mamba fusion, a hybrid architecture that combines the global relational capacity of attention mechanisms with the linear-complexity sequence modeling properties of selective state-space models. The analysis focuses on structural trade-offs rather than algorithmic detail, considering how tokenization strategies, hybrid encoder stages, cross-view fusion, and explanation modules interact under clinical deployment constraints. We discuss explainability infrastructure, including spatial attribution, concept-based auditing, and uncertainty communication, and examine how these mechanisms can support regulatory review and clinician trust. The paper further evaluates data governance, fairness, robustness, sustainability, and operational resilience as first-order design requirements, not post hoc considerations. Cross-domain comparisons with segmentation systems and foundation models are used to highlight architectural choices that influence interpretability and maintainability. We argue that attention-Mamba fusion offers a viable pathway toward explainable cardiovascular disease detection when embedded within a broader socio-technical infrastructure that includes longitudinal data governance, prospective validation, audit trails, and human oversight. The conclusion identifies future research directions related to multi-modal fusion, federated deployment, model drift monitoring, and policy alignment.
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