用贝叶斯脉冲神经网络提升医学图像诊断可靠性，量化预测不确定性。
A Deep Bayesian Convolutional Spiking Neural Network-based CAD system with Uncertainty Quantification for Medical Images Classification
- 基于蒙特卡洛丢弃法构建贝叶斯脉冲网络，实现不确定性量化。
- 在多个医学图像分类任务中准确率优于传统深度学习模型。
- 适合对诊断可靠性要求高的医疗场景，如放射科辅助决策。
计算机辅助诊断（CAD）系统有助于疾病精准诊断。利用第三代神经网络——脉冲神经网络（SNN）的优势，如事件驱动处理、并行性、低功耗及对稀疏时空信息的处理能力，对发展新型CAD系统至关重要。然而，深度脉冲神经网络作为深度学习模型面临可靠性不足的问题，尤其在无法量化预测不确定性时更为突出。为此，本文提出一种基于贝叶斯卷积脉冲神经网络的CAD系统，引入不确定性感知模块。采用蒙特卡洛丢弃法作为贝叶斯近似进行不确定性量化，并在多个医学图像分类任务中验证。实验结果表明，所提模型兼具高精度与可靠性，可作为传统深度学习在医学图像分类中的有效替代方案。
原文摘要 · Abstract (English)
The Computer_Aided Diagnosis (CAD) systems facilitate accurate diagnosis of diseases. The development of CADs by leveraging third generation neural network, namely, Spiking Neural Network (SNN), is essential to utilize of the benefits of SNNs, such as their event_driven processing, parallelism, low power consumption, and the ability to process sparse temporal_spatial information. However, Deep SNN as a deep learning model faces challenges with unreliability. To deal with unreliability challenges due to inability to quantify the uncertainty of the predictions, we proposed a deep Bayesian Convolutional Spiking Neural Network based_CADs with uncertainty_aware module. In this study, the Monte Carlo Dropout method as Bayesian approximation is used as an uncertainty quantification method. This method was applied to several medical image classification tasks. Our experimental results demonstrate that our proposed model is accurate and reliable and will be a proper alternative to conventional deep learning for medical image classification.
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