Lightweight SwiM-UNet with multi-dimensional adaptor for efficient on-device medical image segmentation

For medical image segmentation, transformer-based models have demonstrated superior performance. However, their high computational complexity remains a significant challenge. In contrast, Mamba provides a more computationally efficient alternative, though its segmentation performance is generally inferior to that of transformers. This study proposes a novel lightweight hybrid model based on U-Net, named SwiM-UNet, which represents the first Mamba–transformer hybrid model specifically designed for processing three-dimensional data. Specifically, efficient TSMamba (eTSMamba) blocks are incorporated in the early stages of the U-Net architecture to effectively manage computational overhead, while efficient Swin transformer (eSwin) blocks are employed in the later stages to capture long-range dependencies and local contextual information. Additionally, the model strategically integrates both the Mamba and Swin transformer architectures through a Mamba–Swin adapter (MS-adapter). The proposed MS-adapter comprises three sub-adapters that emphasize local information along the $$x$$-, $$y$$-, and $$z$$-axes, as well as channel-wise features between the eTSMamba and eSwin modules, and includes gating mechanisms to balance the contributions of the sub-adapters. Moreover, a low-rank MLP is utilized in the encoder, and channel reduction is applied in the decoder to further enhance computational efficiency. Performance evaluations conducted on the publicly available BraTS2023 and BraTS2024 datasets demonstrate that the proposed model surpasses state-of-the-art benchmark models while maintaining low computational complexity.