Vision Mamba for Automated Classification of Retinal Diseases from Fundus Images
Keywords:
Vision Mamba, state space models, fundus imaging, diabetic retinopathy, medical image classification, health systems, robustness, fairnessAbstract
Automated classification of retinal diseases from fundus images has become a critical public health application of machine learning because it can expand screening capacity for diabetic retinopathy, glaucoma, age-related macular degeneration, and other sight-threatening conditions. Recent advances in selective state space modeling, especially Vision Mamba, offer a promising alternative to convolutional and attention-based architectures by combining linear-time sequence processing with strong long-range context modeling. This paper presents a system-level analysis of Vision Mamba for retinal disease classification. The discussion moves beyond benchmark accuracy to examine structural trade-offs among local feature extraction, global context integration, scanning path sensitivity, memory footprint, and inference throughput. It further analyzes data governance and infrastructure requirements, including dataset diversity, annotation quality, integration with clinical workflow systems, and versioned model management. The paper addresses robustness and fairness challenges such as hidden stratification, underdiagnosis bias, domain shift across fundus cameras, and the need for subgroup performance auditing. Deployment considerations include cloud and edge operation, continuous monitoring, model updating, regulatory compliance, and environmental sustainability. The analysis positions Vision Mamba as a promising component of ophthalmic screening pipelines but emphasizes that its clinical value depends on governance, transparency, fairness, and human oversight rather than architectural innovation alone. Future directions include federated learning, multimodal foundation models, and policy frameworks for equitable and sustainable ophthalmic artificial intelligence.
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