用Mamba提升医学高光谱图像分割速度与精度
MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation
- 双流结构联合提取空间与光谱特征
- 新序列表示使推理速度最快且资源占用最低
- 适合医学影像分析与实时诊断场景
医学高光谱成像（MHSI）在计算病理学和精准医疗中具有潜力，但现有CNN与Transformer难以兼顾分割精度与速度，因高维空间-光谱特性。本文利用Mamba的全局上下文建模能力，提出一种双流架构实现联合空间-光谱特征提取。为克服Mamba单向聚合的局限，引入循环光谱序列表示，捕获低冗余全局光谱特征。在公开的Multi-Dimensional Choledoch数据集与私有宫颈癌数据集上的实验表明，本方法在分割精度上优于现有最先进模型，同时最小化资源消耗并实现最快推理速度。代码将开源至https://github.com/DeepMed-Lab-ECNU/MDN。
原文摘要 · Abstract (English)
Medical Hyperspectral Imaging (MHSI) offers potential for computational pathology and precision medicine. However, existing CNN and Transformer struggle to balance segmentation accuracy and speed due to high spatial-spectral dimensionality. In this study, we leverage Mamba's global context modeling to propose a dual-stream architecture for joint spatial-spectral feature extraction. To address the limitation of Mamba's unidirectional aggregation, we introduce a recurrent spectral sequence representation to capture low-redundancy global spectral features. Experiments on a public Multi-Dimensional Choledoch dataset and a private Cervical Cancer dataset show that our method outperforms state-of-the-art approaches in segmentation accuracy while minimizing resource usage and achieving the fastest inference speed. Our code will be available at https://github.com/DeepMed-Lab-ECNU/MDN.
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