Efficient Asymmetric Shared Low-Rank Adaptation Based on Selective Scanning Vision Mamba for Medical Imaging Analysis

The Vision Mamba has garnered significant attention for its efficiency and accuracy. Nonetheless, the potential of parameter-efficient tuning in Vision Mamba for medical image analysis remains under-explored. In this work, we investigate the position-sensitive selective scanning mechanism in Vision Mamba and propose a novel low-rank adaptation strategy tailored for planar traversal dependencies. Specifically, we first reshape input patches into sequences following four distinct traversal paths, enabling selective scanning from multiple spatial perspectives. To adapt these paths efficiently, each Visual State Space module is equipped with a shared down-projection matrix and four learnable low-rank up-projection matrices, one for each traversal direction. By combining the shared initial parameters with the direction-specific low-rank components, our approach provides a flexible yet compact way to fine-tune model weights. To further enhance training efficiency, we introduce an iterative optimization strategy that updates the low-rank parameters and task-specific head layers in alternating steps. This strategy encourages faster convergence and reduced computational overhead, making it particularly valuable for large-scale medical image tasks. Extensive experiments on classification, detection, and segmentation benchmarks confirm that our method surpasses existing parameter-efficient approaches.