论文标题
医学图像细分中的不确定性类别：与源相关多样性的研究
Uncertainty categories in medical image segmentation: a study of source-related diversity
论文作者
论文摘要
在深度学习方法的输出中测量不确定性在几种方面有用，例如协助解释产出，帮助对最终用户建立信心，并改善网络的培训和性能。已经提出了几种不同的方法来估计不确定性，包括分别使用测试时间辍学和增强的认知（与所使用的模型有关）和核对源（与数据有关的模型）。这些不确定性源不仅不同，而且由参数设置（例如，辍学率或类型和增强级别）的约束，这些设置建立了更加不同的不确定性类别。这项工作调查了不确定性与这些类别的不同之处以及空间模式的不同，以解决它们是否提供有用的不同信息的问题，每当使用不确定性时应捕获这些信息。我们采取了良好的特征性的Brats挑战数据集，以证明与不同类别的不确定性的大小和空间模式存在实质性差异，并在各种用例中讨论了这些类别的含义。
Measuring uncertainties in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with end users, and for improving the training and performance of the networks. Several different methods have been proposed to estimate uncertainties, including those from epistemic (relating to the model used) and aleatoric (relating to the data) sources using test-time dropout and augmentation, respectively. Not only are these uncertainty sources different, but they are governed by parameter settings (e.g., dropout rate or type and level of augmentation) that establish even more distinct uncertainty categories. This work investigates how different the uncertainties are from these categories, for magnitude and spatial pattern, to empirically address the question of whether they provide usefully distinct information that should be captured whenever uncertainties are used. We take the well characterised BraTS challenge dataset to demonstrate that there are substantial differences in both magnitude and spatial pattern of uncertainties from the different categories, and discuss the implications of these in various use cases.
