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arXiv · 2502.17255

MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation

Shijie Lin·Boxiang Yun·Wei Shen·Qingli Li·Anqiang Yang·Yan Wang
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Abstract

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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Shijie Lin, Boxiang Yun, Wei Shen, Qingli Li, Anqiang Yang, Yan Wang. 2025-02-24. MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation. https://arxiv.org/abs/2502.17255

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