Problem: Medical image segmentation faces critical challenges in balancing global context modeling and computational efficiency. While conventional neural networks struggle with long-range dependencies, Transformers incur quadratic complexity. Although Mamba-based architectures achieve linear complexity, they lack adaptive mechanisms for heterogeneous medical images and demonstrate insufficient local feature extraction capabilities. Method: We propose Linear Context-Aware Robust Mamba (LCAR–Mamba) to address these dual limitations through adaptive resource allocation and enhanced multi-scale extraction. LCAR–Mamba integrates two synergistic modules: the Context-Aware Linear Mamba Module (CALM) for adaptive global–local fusion, and the Multi-scale Partial Dilated Convolution Module (MSPD) for efficient multi-scale feature refinement. Core Innovations: CALM module implements content-driven resource allocation through four-stage processing: (1) analyzing spatial complexity via gradient and activation statistics, (2) computing allocation weights to dynamically balance global and local processing branches, (3) parallel dual-path processing with linear attention and convolution, and (4) adaptive fusion guided by complexity weights. MSPD module employs statistics-based channel selection and multi-scale partial dilated convolutions to capture features at multiple receptive scales while reducing computational cost. Key Results: On ISIC2017 and ISIC2018 datasets, mIoU improvements of 0.81%/1.44% confirm effectiveness across 2D benchmarks. On the Synapse dataset, LCAR–Mamba achieves 85.56% DSC, outperforming the former best Mamba baseline by 0.48% with 33% fewer parameters. Significance: LCAR–Mamba demonstrates that adaptive resource allocation and statistics-driven multi-scale extraction can address critical limitations in linear-complexity architectures, establishing a promising direction for efficient medical image segmentation.
Ma et al. (Sun,) studied this question.