• Corpus ID: 170079074

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

@article{Kohl2019AHP,
  title={A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities},
  author={Simon A. A. Kohl and Bernardino Romera-Paredes and Klaus Maier-Hein and Danilo Jimenez Rezende and S. M. Ali Eslami and Pushmeet Kohli and Andrew Zisserman and Olaf Ronneberger},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.13077}
}
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation. [] Key Method We show that this model formulation enables sampling and reconstruction of segmenations with high fidelity, i.e. with finely resolved detail, while providing the flexibility to learn complex structured distributions across scales. We demonstrate these abilities on the task of segmenting…

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