Problem
Research questions and friction points this paper is trying to address.
Medical Image Segmentation
Lightweight Network
Contextual Modeling
GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation
🤖 AI Summary
本文提出GAD-MambaUNet，通过结合局部建模、方向组状态空间交互和基于DINOv3的梯度自适应蒸馏方法，解决轻量级医学图像分割中的上下文建模问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
GAD-MambaUNet
Direction-Group Graph Selective Scan
Gradient-Adaptive Distillation
DINOv3-GAD supervision
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