Research on the LA-UMamba Model for Asymmetric Modules with Added Auxiliary Information

基于附加辅助信息不对称模块的LA-UMamba模型研究

Deep learning techniques are revolutionizing the developmentof medical image segmentation.With the advancement of Transformer models,especially ViT and Swin-Transformer,which enhances the remote-dependent modeling capability of the model through the self-attention mechanism,better segmentation performance can be achieve.Moreover,the high computational cost of Transformer has motivated researchers to explore more efficient models,such as the Mamba model based on state-space modeling(SSM),and for the field of medical segmentation,reducing the number of model parameters is also necessary.In this study,a novel asymmetric model called LA-UMamba was proposed,which integrates visual Mamba module to efficiently capture complex visual features and remote dependencies.The classical design of U-Net was adopted in the upsampling phase to help reduce the number of references and recover more details.To mitigate the information loss problem,an auxiliary U-Net downsampling layer was designed to focus on sizing without extracting features,thus enhancing the protection of input information while maintaining the efficiency of the model.The experiments were conducted on the ACDC MRI cardiac segmentation dataset,and the results showed that the proposed LA-UMamba achieves proved performance compared to the baseline model in several evaluation metrics,such as IoU,Accuracy,Precision,HD and ASD,which improved that the model is successful in optimizing the detail processing and reducing the complexity of the model,providing a new perspective for further optimization of medical image segmentation techniques.

深度学习技术正在彻底改变医学图像分割的发展。随着Transformer模型的发展,特别是ViT和Swin-Transformer的发展,通过自我关注机制增强了模型的远程建模能力,从而实现了更好的分割性能。此外,Transformer的高计算成本也促使研究人员探索更高效的模型以减少模型参数,如基于状态空间建模(SSM)的Mamba模型。本研究提出了一种新型非对称模型--LA-UMamba,它集成了视觉Mamba模块,能有效捕捉复杂的视觉特征和远程依赖关系。在上采样阶段采用了U-Net的经典设计,以帮助减少参照物的数量并恢复更多细节。为了减少信息丢失问题,设计了一个辅助U-Net下采样层,只关注尺寸而不提取特征,从而在保持模型效率的同时加强了对输入信息的保护。实验在ACDC核磁共振成像心脏分割数据集上进行,结果表明,与基线模型相比,所提出的LA-UMamba在IoU、精度、Hausdorff距离和平均表面距离等多个评价指标上都取得了更好的性能,表明该模型在优化细节处理和降低模型复杂度方面取得了成功。LA-UMamba为进一步优化医学图像分割技术提供了新的视角。