发表机构
Beijing Institute of Petrochemical Technology; Beihang University（北京石油化工学院; 北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出GAD-MambaUNet轻量级医学图像分割网络，通过方向组图选择性扫描和梯度自适应DINOv3蒸馏，在保持高效的同时提升分割精度，并验证了各组件的有效性。
AI 中文摘要
本文提出了GAD-MambaUNet，一种轻量级医学图像分割网络，它结合了高效的局部建模、方向组状态空间交互以及训练时的基础模型监督。为了改善紧凑型分割网络中的上下文建模，我们引入了方向组图选择性扫描（DG-GSS），该机制将扫描方向和通道组响应视为图节点，并在多方向融合之前实现结构化信息交换。我们进一步整合了DINOv3-GAD监督，其中冻结的DINOv3教师模型在训练期间提供语义指导，而梯度自适应蒸馏动态调节蒸馏强度。与具有代表性的轻量级和通用分割方法相比，GAD-MambaUNet实现了良好的精度-效率平衡。消融研究进一步验证了DG-GSS和训练时DINOv3-GAD监督的有效性。在未来的工作中，我们将探索更灵活的师生对齐策略，并将所提出的框架扩展到更多样化的医学分割场景，如多类和多模态分割任务。
英文摘要
In this paper, we proposed GAD-MambaUNet, a lightweight medical image segmentation network that combines efficient local modeling, direction--group state-space interaction, and training-time foundation-model supervision. To improve contextual modeling in compact segmentation networks, we introduced Direction-Group Graph Selective Scan (DG-GSS), which treated scan-direction and channel-group responses as graph nodes and enabled structured information exchange before multi-directional fusion. We further incorporated DINOv3-GAD supervision, where a frozen DINOv3 teacher provided semantic guidance during training, and Gradient-Adaptive Distillation dynamically regulated the distillation strength. GAD-MambaUNet achieves a favorable accuracy--efficiency balance compared with representative lightweight and general segmentation methods. Ablation studies further verify the effectiveness of DG-GSS and training-time DINOv3-GAD supervision. In future work, we will explore more flexible teacher--student alignment strategies and extend the proposed framework to more diverse medical segmentation scenarios, such as multi-class and multi-modal segmentation tasks.
