GaMNet用小模型实现高效脑胶质瘤分割，减少误诊。
GaMNet: A Hybrid Network with Gabor Fusion and NMamba for Efficient 3D Glioma Segmentation
- 结合Gabor滤波与NMamba模块，兼顾局部特征与全局上下文。
- 参数更少、速度更快，误报率和漏报率显著降低。
- 适合部署在移动端医疗设备，临床诊断更可靠。
脑胶质瘤是具有严重健康风险的侵袭性脑肿瘤。深度学习有助于病灶分割，但基于CNN或Transformer的模型常缺乏上下文建模能力或计算量过大，难以在移动医疗设备上实现实时应用。本文提出GaMNet，融合了用于全局建模的NMamba模块与多尺度CNN以实现高效的局部特征提取。为提升可解释性并模拟人类视觉系统，引入多尺度Gabor滤波器。实验表明，该方法在参数更少、计算更快的前提下实现更高分割精度，显著降低假阳性与假阴性，增强临床诊断可靠性。
原文摘要 · Abstract (English)
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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