MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation
Shijie Lin, Boxiang Yun, Wei Shen, Qingli Li, Anqiang Yang, Yan Wang
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.
BibTeX
@inproceedings{icassp2025_mdnmambadrivendu,
title = {MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral Image Segmentation},
author = {Shijie Lin and Boxiang Yun and Wei Shen and Qingli Li and Anqiang Yang and Yan Wang},
booktitle = {ICASSP 2025},
year = {2025}
}