GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation
Journal:
arXiv
Published Date:
May 8, 2025
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.
