Cross-Modal Mamba Networks for Multimodal Brain Tumor Diagnosis Using MRI and Clinical Data

Abstract

Multimodal brain tumor diagnosis requires the joint interpretation of heterogeneous magnetic resonance imaging sequences and non-imaging clinical variables, yet most diagnostic systems remain constrained by unimodal processing or by attention mechanisms whose computational costs grow rapidly with sequence length. This paper examines cross-modal Mamba networks as a system-level alternative for multimodal brain tumor diagnosis, focusing on architectural design, data infrastructure, clinical robustness, governance, fairness, deployment, and sustainability rather than isolated accuracy metrics. Selective state space sequence modeling enables linear-time processing of long multimodal token sequences, making it possible to align MRI-derived visual tokens with variable-length clinical text without the quadratic memory burden of conventional attention. The paper analyzes structural trade-offs in cross-modal fusion, including the ordering of modality encoders, fusion depth, state expansion, and the management of missing clinical data. It further discusses the institutional and regulatory conditions needed for safe translation, including federated learning, demographic fairness auditing, post hoc interpretability, prospective validation, and energy-conscious deployment. By treating cross-modal Mamba architectures as socio-technical infrastructures, the discussion connects computational design choices to broader concerns of clinical accountability, data governance, and health system sustainability. The paper argues that the value of such architectures lies not only in their efficiency but also in their capacity to support auditable, robust, and equitable multimodal diagnosis across heterogeneous hospital environments.