U-Net: Convolutional Networks for Biomedical Image Segmentation

@inproceedings{Ronneberger2015UNetCN,
  title={U-Net: Convolutional Networks for Biomedical Image Segmentation},
  author={O. Ronneberger and P. Fischer and T. Brox},
  booktitle={MICCAI},
  year={2015}
}
There is large consent that successful training of deep networks requires many thousand annotated training samples. [...] Key Method We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge…Expand
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  • Shuo-Wen Chang, S. Liao
  • Computer Science
  • 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
  • 2019
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