迈向安全的深度学习：准确量化神经网络预测中的生物标志物不确定性
Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
摘要：
自动医学图像分割，特别是使用深度学习，在语义分割任务中表现出出色的性能。然而，这些方法很少量化其不确定性，这可能导致下游分析的错误。在这项工作中，我们建议使用贝叶斯神经网络来量化语义分割域内的不确定性。我们还提出了一种方法，体素分割的不确定性转换为体积的不确定性，并校准的准确性和可靠性的置信区间的衍生测量。当应用于肿瘤体积估计应用时，我们证明了通过使用这种不确定性建模，可以使深度学习系统报告具有良好校准的误差条的体积估计，使其更安全地用于临床使用。我们还表明，不确定性估计外推到看不见的数据，并且在人工噪声的存在下，置信区间是鲁棒的。这可以用来提供一种质量控制和质量保证的形式，并且可以允许在临床中进一步采用深度学习工具。
英文摘要：
Automated medical image segmentation, specifically using deep learning, has shown outstanding performance in semantic segmentation tasks. However, these methods rarely quantify their uncertainty, which may lead to errors in downstream analysis. In this work we propose to use Bayesian neural networks to quantify uncertainty within the domain of semantic segmentation. We also propose a method to convert voxel-wise segmentation uncertainty into volumetric uncertainty, and calibrate the accuracy and reliability of confidence intervals of derived measurements. When applied to a tumour volume estimation application, we demonstrate that by using such modelling of uncertainty, deep learning systems can be made to report volume estimates with well-calibrated error-bars, making them safer for clinical use. We also show that the uncertainty estimates extrapolate to unseen data, and that the confidence intervals are robust in the presence of artificial noise. This could be used to provide a form of quality control and quality assurance, and may permit further adoption of deep learning tools in the clinic.
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