On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

@article{Li2017OnTC,
  title={On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task},
  author={Wenqi Li and Guotai Wang and Lucas Fidon and S{\'e}bastien Ourselin and M. Jorge Cardoso and Tom Vercauteren},
  journal={ArXiv},
  year={2017},
  volume={abs/1707.01992}
}
Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate… CONTINUE READING
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