Robust uncertainty estimations are necessary in safety-critical applications of Deep Learning. One such example is the semantic segmentation of medical images, whilst deep-learning approaches have high performance in such tasks they lack interpretability as they give no indication of their confidence when making classification decisions. Robust and interpretable segmentation is a critical first stage in automatically screening for pathologies hence the optimal solution is one which can provide high accuracy but also capture the underlying uncertainty. In this work, we present an uncertainty-aware segmentation model, BA U-Net, for use on MRI data that incorporates Bayesian Neural Networks and Attention Mechanisms to provide accurate and interpretable segmentations. We evaluated our model on the publicly available BraTS 2020 dataset using F1 Score and Intersection Over Union (IoU) as evaluation metrics.

翻译：在深度学习的医疗安全关键应用中，鲁棒的不确定性估计至关重要。以医学图像语义分割为例，深度学习方法虽在此类任务中表现优异，但缺乏可解释性——其在做出分类决策时无法指示置信程度。鲁棒且可解释的分割是自动筛查病理的关键初始环节，因此最优方案需兼顾高精度与对潜在不确定性的捕捉能力。本文提出一种面向MRI数据的不确定性感知分割模型BA U-Net，该模型融合贝叶斯神经网络与注意力机制，可提供精准且可解释的分割结果。我们采用F1分数与交并比(IoU)作为评估指标，在公开的BraTS 2020数据集上对模型进行了验证。