# Reliable Brain Tumor Segmentation Based on Spiking Neural Networks with Efficient Training

arXiv: https://arxiv.org/abs/2601.16652 · HTML: https://mlsanity.com/paper/2601.16652/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 24 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications](https://mlsanity.com/paper/2007.05657/) (2020)
- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [A Spiking Neural Network for Image Segmentation](https://mlsanity.com/paper/2106.08921/) (2021)
- [HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities](https://mlsanity.com/paper/2211.10754/) (2022)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)](https://mlsanity.com/paper/2305.09011/) (2023)
- [Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approach](https://mlsanity.com/paper/2507.03765/) (2025)
- [Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation](https://mlsanity.com/paper/2302.07328/) (2023)
- [Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout](https://mlsanity.com/paper/2304.10191/) (2023)
- [Average-Over-Time Spiking Neural Networks for Uncertainty Estimation in Regression](https://mlsanity.com/paper/2412.00278/) (2024)

## Abstract (arXiv)

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.