Discriminative convolutional neural networks (CNNs), for which a voxel-wise conditional Multinoulli distribution is assumed, have performed well in many brain lesion segmentation tasks. For a trained discriminative CNN to be used in clinical practice, the patient's radiological features are inputted into the model, in which case a conditional distribution of segmentations is produced. Capturing the uncertainty of the predictions can be useful in deciding whether to abandon a model, or choose amongst competing models. In practice, however, we never know the ground truth segmentation, and therefore can never know the true model variance. In this work, segmentation sampling on discriminative CNNs is used to assess a trained model's robustness by analyzing the inter-sample Dice distribution on a new patient solely based on their magnetic resonance (MR) images. Furthermore, by demonstrating the inter-sample Dice observations are independent and identically distributed with a finite mean and variance under certain conditions, a rigorous confidence based decision rule is proposed to decide whether to reject or accept a CNN model for a particular patient. Applied to the ISLES 2015 (SISS) dataset, the model identified 7 predictions as non-robust, and the average Dice coefficient calculated on the remaining brains improved by 12 percent.

翻译：诊断性神经神经网络(CNNs)在很多脑损伤分化任务中表现良好。为了在临床实践中使用训练有素的有区别的CNN, 将病人的放射特征输入模型, 从而产生有条件的分化分布。 掌握预测的不确定性有助于决定是否放弃模型, 或选择相互竞争的模式。 然而,在实践中, 我们从来不知道地面真实分化, 因此永远无法知道真正的模型差异。 在这项工作中, 歧视型CNNs的分化抽样抽样用于评估经过培训的模式的强度, 分析新病人的跨抽样Dice分布, 仅以其磁共振图像为基础。 此外, 通过展示相隔层Dice观测是独立的, 在某些条件下, 以有限的平均值和差异来进行同样的分配。 我们建议了严格的基于信任的决定规则, 以决定是否拒绝或接受特定病人的CNN模型。 使用有区别的有区别的有区别的有区别的有区别的CNNCNN, 在这次工作中, 使用有区别的有区别的有区别的有区别的有区别的有区别的有区别的有区别的有区别的有区别的有区别的有区别, 用于评估模型的模型的模型的强度,, 分析对新病人的分布式Dicedicedice的分布分布分布分布的分布的分布的分布进行分析, 仅的分布为12 的模型是算算算算的模型, 。