mlsanityData to 7 Oct 2026

Effect of latent space distribution on the segmentation of images with multiple annotations

arXiv 2304.13476 ↗First posted 26 Apr 2023cs.CV cs.LG eess.IVcited by 7

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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 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

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 26 of its 48 references that are in the map. First posted on arXiv 26 Apr 2023.

Builds on

26 of 48 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

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

7 papers.

  1. 2024A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentationcited by 6
  2. 2023Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networkscited by 3
  3. 2024Towards Collective Intelligence: Uncertainty-aware SAM Adaptation for Ambiguous Medical Image Segmentationcited by 3
  4. 2023Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imagingcited by 2
  5. 2025Diffusion Based Ambiguous Image Segmentationcited by 1
  6. 2024Multi-style conversion for semantic segmentation of lesions in fundus images by adversarial attackscited by 0
  7. 2026Towards Agnostic and Holistic Universal Image Segmentation with Bit Diffusioncited by 0