The document discusses a probabilistic U-Net model for segmenting ambiguous images. It introduces a probabilistic U-Net that combines a U-Net with a variational autoencoder (VAE) to model segmentation as a probabilistic inference task. The probabilistic U-Net uses the U-Net to encode an input image into a latent space and decode a segmentation map, while the VAE models the latent space as a probability distribution to account for ambiguity. It is evaluated on the CityScapes dataset for segmenting lung abnormalities and other medical images.

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2018/10/29

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3. probabilistic U-Net
S. Kohl, et. al. “A Probabilistic U-Net for Segmentation of Ambiguous
Images ”
arXiv
https://arxiv.org/abs/1806.05034
my Qiita
https://qiita.com/masataka46/items/61615e069c03049aecff
GitHub tensorflow
https://github.com/SimonKohl/probabilistic_unet
my GitHub tensorflow
https://github.com/masataka46/probabilistic_UNet

4. probabilistic U-Net
Simon A. A. Kohl DeepMind
segmentation

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CityScapes dataset
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