mlsanityData to 14 Oct 2026

Generalized Probabilistic U-Net for medical image segementation

arXiv 2207.12872 ↗First posted 26 Jul 2022cs.CV cs.LGcited by 10

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Abstract (arXiv)

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

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

19 of 28 references.

References · 19

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7 more
  1. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 220
  2. 2019PHiSeg: Capturing Uncertainty in Medical Image Segmentationcited by 128
  3. 2019A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguitiescited by 65
  4. 2018On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentationcited by 46
  5. 2019Supervised Uncertainty Quantification for Segmentation with Multiple Annotationscited by 42
  6. 2020Uncertainty quantification in medical image segmentation with normalizing flowscited by 23
  7. 2021Improving Aleatoric Uncertainty Quantification in Multi-Annotated Medical Image Segmentation with Normalizing Flowscited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 1; 2026: 2.

10 papers.

  1. 2023Ambiguous Medical Image Segmentation using Diffusion Modelscited by 92
  2. 2024FlowSDF: Flow Matching for Medical Image Segmentation Using Distance Transformscited by 25
  3. 2024Tyche: Stochastic In-Context Learning for Medical Image Segmentationcited by 20
  4. 2023Effect of latent space distribution on the segmentation of images with multiple annotationscited by 7
  5. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  6. 2023Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imagingcited by 2
  7. 2025Diffusion Based Ambiguous Image Segmentationcited by 1
  8. 2024Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantificationcited by 0
  9. 2026Towards Agnostic and Holistic Universal Image Segmentation with Bit Diffusioncited by 0
  10. 2026Neural Architecture Distributions: A New Paradigm for Stochastic Segmentationcited by 0