用脉冲神经网络实现低功耗脑肿瘤分割，精度高且可估不确定性。
Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Training
- 多视角脉冲网络集成，提升分割鲁棒性并输出像素级不确定性。
- 采用FPTT训练方法，计算量降低87%（FLOPs），保持高效学习。
- 适合医疗物联网与床旁设备，兼顾精度与能效，适合嵌入式部署。
我们提出一种基于脉冲神经网络（SNNs）的可靠且低功耗3D脑肿瘤分割框架。通过矢状、冠状、轴向三个视角的SNN模型集成，实现像素级不确定性估计，增强分割鲁棒性。为解决SNN在语义图像分割中训练成本高的问题，采用前向时序传播（FPTT），在显著降低计算开销的同时保持时间学习效率。在BraTS 2017和BraTS 2023数据集上的实验表明，该方法具备竞争力的分割精度、校准良好的不确定性估计，且计算量减少87%（FLOPs），验证了SNN在可靠、低功耗医疗物联网与床旁系统中的应用潜力。
原文摘要 · Abstract (English)
We propose a reliable and energy-efficient framework for 3D brain tumor segmentation using spiking neural networks (SNNs). A multi-view ensemble of sagittal, coronal, and axial SNN models provides voxel-wise uncertainty estimation and enhances segmentation robustness. To address the high computational cost in training SNN models for semantic image segmentation, we employ Forward Propagation Through Time (FPTT), which maintains temporal learning efficiency with significantly reduced computational cost. Experiments on the Multimodal Brain Tumor Segmentation Challenges (BraTS 2017 and BraTS 2023) demonstrate competitive accuracy, well-calibrated uncertainty, and an 87% reduction in FLOPs, underscoring the potential of SNNs for reliable, low-power medical IoT and Point-of-Care systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。