Generalized Probabilistic U-Net for medical image segementation

2022/07/26 by Ishaan Bhat, Josien P. W. Pluim, Bhat, Ishaan +3 · 1 citation
Computer Science · #AI in cancer detection #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Medical Image Segmentation Techniques

paper · doi:10.48550/arxiv.2207.12872

openalex publication_date 2022/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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

Cited by

• VHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction

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