We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that using a mixture of Gaussians results in a statistically significant improvement in the generalized energy distance (GED) metric with respect to the standard Probabilistic U-Net. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet

翻译：我们建议采用通用的概率概率U-Net,扩大概率U-网络,允许将高斯分布的更普遍的形式作为潜在空间分布,以更好地估计参考区段的不确定性;我们研究潜在空间分布的选择对利用LIDC-IDRI数据集捕捉参考区段不确定性的影响;我们表明,分配的选择影响预测的样本多样性及其与参考区段的重叠;关于LIDC-IDRI数据集,我们表明,使用高斯人混合使用高斯人,在统计上大大改进通用能源距离(GED)标准概率U-Net的衡量标准。我们已经在https://github.comshaanb92/genizedProbabiliticUNet上公布了我们的实施情况。