Convolutional neural networks (CNNs), which have been the state-of-the-art in many image-related applications, are prone to losing important spatial information between image instances. Capsule networks (CapsNets), on the other hand, are capable of leveraging such information through their routing by agreement process, making them powerful architectures for small datasets, such as medical imaging ones. Within the domain of medical imaging problems, brain tumor classification is of paramount importance, due to the deadly nature of this cancer and the consequences of the tumor misclassification. In our recent works, we showed potentials of developing CapsNet architecture for the task of brain tumor type classification. Similar to other deep learning models, however, CapsNets do not capture prediction uncertainty (coming from the uncertainty in the model weights, which is significantly important in keeping the human experts in the loop, by returning the uncertain samples. In this paper, we propose a Bayesian CapsNet framework, referred to as the $\text{BayesCap}$, that can provide not only the mean predictions, but also entropy as a measure of prediction uncertainty. Results show that filtering out the uncertain predictions can improve the accuracy, confirming that returning the uncertain predictions is an appropriate strategy for improving interpretability of the network.

中文翻译：

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BayesCap：使用胶囊网络进行脑肿瘤分类的贝叶斯方法

卷积神经网络 (CNN) 在许多与图像相关的应用中一直是最先进的，但容易丢失图像实例之间的重要空间信息。另一方面，胶囊网络 (CapsNets) 能够通过协议过程的路由来利用这些信息，使其成为适用于小型数据集（例如医学成像数据集）的强大架构。在医学成像问题的领域内，由于脑肿瘤的致命性质和肿瘤错误分类的后果，脑肿瘤分类至关重要。在我们最近的工作中，我们展示了为脑肿瘤类型分类任务开发 CapsNet 架构的潜力。然而，与其他深度学习模型类似，这不仅可以提供平均预测，还可以提供熵作为预测不确定性的度量。结果表明，过滤掉不确定的预测可以提高准确性，证实返回不确定的预测是提高网络可解释性的合适策略。这不仅可以提供平均预测，还可以提供熵作为预测不确定性的度量。结果表明，过滤掉不确定的预测可以提高准确性，证实返回不确定的预测是提高网络可解释性的合适策略。