Abstract / Summary
Accurate and efficient polyp segmentation is essential for computer-aided colorectal cancer screening, yet achieving a favorable balance among segmentation accuracy, computational efficiency, and cross-dataset generalization remains challenging. Recent studies suggest that Mamba-based state-space modeling can enhance long-range dependency modeling with relatively low complexity, but how lightweight Mamba integration strategies affect dense prediction frameworks for polyp segmentation remains insufficiently understood. In this study, we present LiteMamba-Seg, a lightweight polyp segmentation framework built on a U-Net-like encoder–decoder architecture with a ResNet34 backbone, and conduct an empirical evaluation of different lightweight Mamba integration strategies. Specifically, we investigate bottleneck-only integration as well as alternative second-stage placements at an encoder-side intermediate stage and a decoder-side reconstruction stage. Experimental results on four public benchmark datasets show that LiteMamba-Seg achieves competitive segmentation performance while maintaining a lightweight design and real-time model-level inference, with 26.81 M parameters, 13.89 GFLOPs, and 167.73 FPS. Further ablation, controlled placement, multi-seed, robustness, boundary, calibration, and false-positive analyses indicate that the effect of Mamba integration depends on its placement within the network. In particular, decoder-side reconstruction-stage integration yields more favorable quantitative and qualitative results than encoder-side intermediate placement across the evaluated benchmarks. These findings suggest that, for lightweight polyp segmentation frameworks, Mamba integration should be considered jointly with placement strategy rather than simply increasing the number of sequence modeling blocks.