DSVM-UNet : 通过双自我蒸馏增强VM-UNet用于医学图像分割
DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation
AI总结：
DSVM-UNet通过双自我蒸馏改进VM-UNet，提升医学图像分割的性能与效率。
摘要：
视觉Mamba模型已在各个领域得到了广泛研究，通过线性时间开销有效管理长距离依赖关系来解决先前模型的局限性。几项前瞻性研究进一步设计了基于UNet（VM-UNet）的视觉Mamba模型用于医学图像分割。这些方法主要通过创建更复杂的结构来优化架构设计，以增强模型感知语义特征的能力。在本文中，我们提出了一种简单而有效的途径，通过双自我蒸馏改进VM-UNet（DSVM-UNet），而无需任何复杂的架构设计。为了实现这一目标，我们开发了双自我蒸馏方法，以在全局和局部层面对齐特征。在ISIC2017、ISIC2018和Synapse基准测试中进行的大量实验表明，我们的方法在保持计算效率的同时实现了最先进的性能。代码可在https://github.com/RoryShao/DSVM-UNet.git上获得。
英文摘要：
Vision Mamba models have been extensively researched in various fields, which address the limitations of previous models by effectively managing long-range dependencies with a linear-time overhead. Several prospective studies have further designed Vision Mamba based on UNet(VM-UNet) for medical image segmentation. These approaches primarily focus on optimizing architectural designs by creating more complex structures to enhance the model's ability to perceive semantic features. In this paper, we propose a simple yet effective approach to improve the model by Dual Self-distillation for VM-UNet (DSVM-UNet) without any complex architectural designs. To achieve this goal, we develop double self-distillation methods to align the features at both the global and local levels. Extensive experiments conducted on the ISIC2017, ISIC2018, and Synapse benchmarks demonstrate that our approach achieves state-of-the-art performance while maintaining computational efficiency. Code is available at https://github.com/RoryShao/DSVM-UNet.git.
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。
