用双重自蒸馏提升医学图像分割模型性能，不增加复杂结构
DSVM-UNet : Enhancing VM-UNet with Dual Self-distillation for Medical Image Segmentation
- 通过全局与局部双重自蒸馏对齐特征
- 在ISIC2017/2018和Synapse上达领先效果
- 适合追求高效高精度分割的医疗AI研究者
视觉马尔可夫模型在多个领域得到广泛研究，其通过线性时间开销有效处理长程依赖。已有研究基于UNet设计了视觉马尔可夫模型（VM-UNet）用于医学图像分割，主要通过构建更复杂的结构来增强语义特征感知能力。本文提出一种无需复杂结构设计的简单而有效方法——为VM-UNet引入双重自蒸馏（DSVM-UNet），在不改变架构的前提下实现性能提升。通过双自蒸馏机制，在全局与局部层面实现特征对齐。在ISIC2017、ISIC2018和Synapse基准上的大量实验表明，该方法在保持计算效率的同时达到当前最优性能。代码已公开于https://github.com/RoryShao/DSVM-UNet.git。
原文摘要 · Abstract (English)
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.
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