The Importance of Skip Connections in Biomedical Image Segmentation

Autor:	Drozdzal, Michal, Vorontsov, Eugene, Chartrand, Gabriel, Kadoury, Samuel, Pal, Chris
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to 2nd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2016); Added references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1608.04117 Zobrazit plný text záznamu View this record from Arxiv