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

TL;DR

This paper introduces MDN, a dual-stream neural network leveraging Mamba's global context modeling and recurrent spectral sequence representation to improve medical hyperspectral image segmentation accuracy and speed, outperforming existing methods.

Contribution

The paper proposes a novel dual-stream architecture with recurrent spectral features and Mamba-based global context modeling for enhanced hyperspectral image segmentation.

Findings

Outperforms state-of-the-art methods in accuracy

Achieves faster inference speed

Reduces resource usage during segmentation

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.