Learning normalized inputs for iterative estimation in medical image segmentation

@article{Drozdzal2018LearningNI,
  title={Learning normalized inputs for iterative estimation in medical image segmentation},
  author={Michal Drozdzal and Gabriel Chartrand and Eugene Vorontsov and Mahsa Shakeri and Lisa Di-Jorio and An Tang and Adriana Romero and Yoshua Bengio and Christopher Joseph Pal and Samuel Kadoury},
  journal={Medical image analysis},
  year={2018},
  volume={44},
  pages={
          1-13
        }
}
In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can… CONTINUE READING
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