The Importance of Skip Connections in Biomedical Image Segmentation
This work addresses biomedical image segmentation, which is incremental as it builds on existing FCN and residual network architectures.
The authors tackled the problem of biomedical image segmentation by studying the influence of long and short skip connections in Fully Convolutional Networks (FCNs), showing that a very deep FCN with both types of connections achieves near-to-state-of-the-art results on the EM dataset without post-processing.
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.