使用高级Unet进行医学图像分割：VMSE-Unet与VM-Unet CBAM+
Medical Image Segmentation Using Advanced Unet: VMSE-Unet and VM-Unet CBAM+
AI总结：
本文提出VMSE-Unet和VM-Unet CBAM+两种融合注意力机制的U-Net变体，显著提升医学图像分割精度与效率，其中VMSE-Unet性能最优，具有临床应用的潜力。
摘要：
本文提出了VMSE U-Net和VM-Unet CBAM+模型，这两种尖端的深度学习架构旨在增强医学图像分割。我们的方法将Squeeze-and-Excitation（SE）和Convolutional Block Attention Module（CBAM）技术整合到传统的VM U-Net框架中，显著提高了分割精度、特征定位和计算效率。与基线VM-Unet相比，这两个模型在多个数据集上均表现出更优的性能。值得注意的是，VMSEUnet在保持低损失值的同时，实现了最高的准确率、IoU、精确率和召回率。它还在GPU和CPU上展现出卓越的计算效率，具有更快的推理时间和更低的内存占用。总体而言，该研究表明增强型架构VMSE-Unet是医学图像分析的有价值工具。这些发现突显了其在真实世界临床应用中的潜力，强调了进一步研究以优化准确性、鲁棒性和计算效率的重要性。
英文摘要：
In this paper, we present the VMSE U-Net and VM-Unet CBAM+ model, two cutting-edge deep learning architectures designed to enhance medical image segmentation. Our approach integrates Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) techniques into the traditional VM U-Net framework, significantly improving segmentation accuracy, feature localization, and computational efficiency. Both models show superior performance compared to the baseline VM-Unet across multiple datasets. Notably, VMSEUnet achieves the highest accuracy, IoU, precision, and recall while maintaining low loss values. It also exhibits exceptional computational efficiency with faster inference times and lower memory usage on both GPU and CPU. Overall, the study suggests that the enhanced architecture VMSE-Unet is a valuable tool for medical image analysis. These findings highlight its potential for real-world clinical applications, emphasizing the importance of further research to optimize accuracy, robustness, and computational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
