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

• URL: http://arxiv.org/abs/2505.05520v1
• Date: Thu, 08 May 2025 04:25:22 GMT
• Title: GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation
• Authors: Chengwei Ye, Huanzhen Zhang, Yufei Lin, Kangsheng Wang, Linuo Xu, Shuyan Liu,
• Abstract summary: Deep learning aids in lesion segmentation, but CNN and Transformer-based models often lack context modeling or demand heavy computation.<n>We propose GaMNet, integrating the NMamba module for global modeling and a multi-scale CNN for efficient local feature extraction.<n>Our method achieves high segmentation accuracy with fewer parameters and faster computation.
• Score: 2.3649376465820384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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