Learning normalized inputs for iterative estimation in medical image segmentation

  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},
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
Highly Cited
This paper has 72 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 7 times over the past 90 days. VIEW TWEETS


Publications citing this paper.
Showing 1-10 of 16 extracted citations

72 Citations

Citations per Year
Semantic Scholar estimates that this publication has 72 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 53 references

Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude

  • T. Tieleman, G. Hinton
  • COURSERA: Neural Networks for Machine Learning,
  • 2012
Highly Influential
6 Excerpts

Multimodal tumor segmentation with 3d volumetric convolutional neural networks

  • B. Pandian, J. Boyle, D. A. Orringer
  • In Proceedings of the MICCAI Challenge on…
  • 2016
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…