论文标题
用于医学图像分离的广义概率U-NET
Generalized Probabilistic U-Net for medical image segementation
论文作者
论文摘要
我们提出了广义的概率U-NET，该概率U-NET通过将高斯分布的更通用形式作为潜在空间分布来扩展概率U-NET，可以更好地近似参考分段中的不确定性。我们研究了潜在空间分布对使用LIDC-IDRI数据集捕获参考分割的不确定性的效果。我们表明，分布的选择会影响预测的样本多样性及其相对于参考分割的重叠。对于LIDC-IDRI数据集，我们表明，使用高斯人的混合物会导致广义能量距离（GED）度量相对于标准概率U-NET的统计显着改善。我们已经在https://github.com/ishaanb92/generalizedprobabilisticunet上提供了实施。
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
