Iterative deep convolutional encoder-decoder network for medical image segmentation

@article{Kim2017IterativeDC,
  title={Iterative deep convolutional encoder-decoder network for medical image segmentation},
  author={J. U. Kim and Hak Gu Kim and Yong Man Ro},
  journal={2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2017},
  pages={685-688}
}
  • J. U. Kim, Hak Gu Kim, Yong Man Ro
  • Published 2017
  • Computer Science
  • 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
In this paper, we propose a novel medical image segmentation using iterative deep learning framework. [...] Key Method The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has…Expand
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