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 variation in the reference segmentations for lung tumors and white matter hyperintensities in the brain. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet

翻译：我们提出了广义概率U-Net，该方法将概率U-Net进行扩展，允许使用更一般形式的高斯分布作为潜在空间分布，从而更好地逼近参考分割中的不确定性。我们研究了潜在空间分布的选择对捕捉肺肿瘤及脑白质高信号区域参考分割变异性的影响，结果表明分布选择会影响预测的样本多样性及其与参考分割的重叠程度。我们的实现已公开于 https://github.com/ishaanb92/GeneralizedProbabilisticUNet