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HEp-2 Cell Classification Using K-Support Spatial Pooling in Deep CNNs Read chapter Read first chapter

2016 | OriginalPaper | Chapter

The Importance of Skip Connections in Biomedical Image Segmentation

Authors : Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury, Chris Pal

Published in: Deep Learning and Data Labeling for Medical Applications

Publisher: Springer International Publishing

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Abstract

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.

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previous chapter Estimating CT Image from MRI Data Using 3D Fully Convolutional Networks
next chapter Understanding the Mechanisms of Deep Transfer Learning for Medical Images
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Metadata
Title
The Importance of Skip Connections in Biomedical Image Segmentation
Authors
Michal Drozdzal
Eugene Vorontsov
Gabriel Chartrand
Samuel Kadoury
Chris Pal
Copyright Year
2016
Book
Deep Learning and Data Labeling for Medical Applications

Print ISBN: 978-3-319-46975-1

Electronic ISBN: 978-3-319-46976-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46976-8_19

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