Mamba is a neural network architecture that reduces computational complexity, and has been shown to be effective for 3D medical image segmentation. However, existing models still remain computationally expensive. We propose compressing Mamba by distillation while achieving competitive performance. Specifically, we demonstrate compression of a 139M-parameter SegMamba-V2 teacher into a lightweight TinyUNet3D student with about 0.96M parameters, achieving about 145$\times$ compression with a performance drop of just over one Dice point. We further investigate the utility of knowledge distillation in a range of data-labeling scenarios under identical experimental settings, and show that its advantage is most prominent when labeled data is scarce. To demonstrate the generalizability of our approach, experiments are conducted on three datasets: BraTS 2023, MSD Spleen and MSD Hippocampus. The code for TinyUNet3D is publicly available at: https://github.com/2802990u/tinyunet3d-dist-kd.
Rahim Ullah, Hasan Abbas, Hazrat Ali, Syed Waqar Nabi
原文摘要
Mamba has the capability to capture global context in linear time for 3D medical image segmentation. However, existing methods employ Mamba with full-width directional scans at every resolution, keeping the model large (64-138M parameters and high FLOPs). We propose Grouped Multi-Directional (GMD) Mamba, a lightweight network based on the GMD scan that divides input channels $C$ into groups $G$ and flattens each group in different directions in one pass. This gives multi-directional context at $O(C^2/G)$ instead of $O(K \cdot C^2)$, where $K$ is the number of scan directions. Additionally, we employ Mamba only at the deepest encoder stages, resulting in 19.2M parameters. On BraTS2023, it achieves the best average Dice of 92.0% and the lowest HD95 of 2.7 at $\sim$1/7 the parameters of SegMamba-V2. For model validation, we also ran experiments on the MSD Spleen dataset, achieving competitive performance. The implementation details and code are publicly available at https://github.com/2802990u/GMD-Mamba.