Generative Mamba Models for Data Augmentation in Low-Resource Medical Imaging
Keywords:
generative state space models; Mamba; medical imaging; data augmentation; low-resource learning; algorithmic governanceAbstract
Low-resource medical imaging environments face a persistent tension between the need for high-capacity deep learning models and the limited availability of expertly annotated data. Traditional augmentation methods can improve generalization but rarely introduce the semantic diversity required for rare diseases, underrepresented anatomies, and heterogeneous acquisition protocols. Generative models have been proposed as a response to this limitation, yet their deployment in clinical and low-resource settings is often constrained by computational cost, training instability, and governance complexity. This paper examines generative Mamba models as a system-level alternative for data augmentation in medical imaging. Mamba architectures use selective state space sequence modeling to provide efficient long-range dependency modeling, making them attractive for high-resolution image synthesis under constrained computational budgets. The discussion positions data augmentation not as an isolated algorithmic step but as an integrated socio-technical pipeline spanning data curation, generative pretraining, downstream validation, auditing, deployment, and regulatory oversight. The paper analyzes architectural trade-offs between adversarial, variational, diffusion, and state space generative approaches and examines how Mamba-based backbones may reduce memory and inference costs while preserving clinically meaningful variation. It further addresses robustness risks such as mode collapse, shortcut learning, demographic bias amplification, and synthetic data provenance. Infrastructure considerations include federated training, edge deployment, interoperability with clinical imaging systems, and sustainability. The paper argues that generative Mamba models can support low-resource medical imaging, but only when embedded within transparent, auditable, and clinically validated augmentation systems.
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