GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation

TL;DR

GaMNet is a hybrid deep learning model combining Gabor filters, NMamba global modeling, and multi-scale CNNs to achieve efficient, accurate 3D glioma segmentation suitable for real-time clinical applications.

Contribution

It introduces GaMNet, a novel architecture that integrates Gabor filters and NMamba modules for improved efficiency and interpretability in glioma segmentation.

Findings

Outperforms existing methods in accuracy

Reduces false positives and negatives

Fewer parameters and faster computation

Abstract

Gliomas are aggressive brain tumors that pose serious health risks. Deep learning aids in lesion segmentation, but CNN and Transformer-based models often lack context modeling or demand heavy computation, limiting real-time use on mobile medical devices. We propose GaMNet, integrating the NMamba module for global modeling and a multi-scale CNN for efficient local feature extraction. To improve interpretability and mimic the human visual system, we apply Gabor filters at multiple scales. Our method achieves high segmentation accuracy with fewer parameters and faster computation. Extensive experiments show GaMNet outperforms existing methods, notably reducing false positives and negatives, which enhances the reliability of clinical diagnosis.