改进U-Net模型，提升医学图像分割精度与效率
Medical Image Segmentation Using Advanced Unet: VMSE-Unet and VM-Unet CBAM+
- 融合SE与CBAM注意力机制，增强特征提取能力
- VMSE-Unet在多数据集上达最高准确率与交并比
- 推理更快、内存更低，适合临床实时应用
本文提出VMSE-U-Net和VM-Unet CBAM+两种先进深度学习架构，用于提升医学图像分割性能。通过将Squeeze-and-Excitation（SE）与卷积块注意力模块（CBAM）引入传统VM-U-Net框架，显著提高分割精度、特征定位能力及计算效率。两种模型在多个数据集上均优于基线VM-Unet。其中，VMSE-Unet在准确率、交并比（IoU）、精确率和召回率上表现最佳，同时保持较低损失值。其在GPU与CPU上均展现出优异的计算效率，推理速度更快，内存占用更低。研究结果表明，改进后的VMSE-Unet是医学图像分析的重要工具，具有广阔临床应用前景，未来可进一步优化准确性、鲁棒性与效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。