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.
M. Drozdzal and E. Vorontsov—Equal contribution.
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Acknowledgements
We would like to thank all the developers of Theano and Keras for providing such powerful frameworks. We gratefully acknowledge NVIDIA for GPU donation to our lab at École Polytechnique. The authors would like to thank Lisa di Jorio, Adriana Romero and Nicolas Chapados for insightful discussions. This work was partially funded by Imagia Inc., MITACS (grant number IT05356) and MEDTEQ.
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Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C. (2016). The Importance of Skip Connections in Biomedical Image Segmentation. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_19
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