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.
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Cite this article
APA 7
Wang, F., Li, H., Chao, W., Zhuo, Z., & Yang, X. (2026). GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation. https://omanscience.com/en/articles/gad-mambaunet-direction-group-mamba-with-gradient-adaptive-dinov3-distillation-for-lightweight-medical-image-segmentation
MLA 9
Wang, Fang, et al. "GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation." https://omanscience.com/en/articles/gad-mambaunet-direction-group-mamba-with-gradient-adaptive-dinov3-distillation-for-lightweight-medical-image-segmentation.
Chicago (author–date)
Wang, Fang, Huitao Li, Wenhan Chao, Zheng Zhuo, and Xinxin Yang. 2026. "GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation." https://omanscience.com/en/articles/gad-mambaunet-direction-group-mamba-with-gradient-adaptive-dinov3-distillation-for-lightweight-medical-image-segmentation.
Harvard
Wang, F., Li, H., Chao, W., Zhuo, Z. and Yang, X. (2026) 'GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation', Available at: https://omanscience.com/en/articles/gad-mambaunet-direction-group-mamba-with-gradient-adaptive-dinov3-distillation-for-lightweight-medical-image-segmentation.
Vancouver
Wang F, Li H, Chao W, Zhuo Z, Yang X. GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation. https://omanscience.com/en/articles/gad-mambaunet-direction-group-mamba-with-gradient-adaptive-dinov3-distillation-for-lightweight-medical-image-segmentation
IEEE
F. Wang, H. Li, W. Chao, Z. Zhuo, and X. Yang, "GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation," https://omanscience.com/en/articles/gad-mambaunet-direction-group-mamba-with-gradient-adaptive-dinov3-distillation-for-lightweight-medical-image-segmentation.