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2016

The Importance of Skip Connections in Biomedical Image Segmentation

Drozdzal, Michal, Vorontsov, Eugene, Chartrand, Gabriel et al.

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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.

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Styner, M., Lee, J., Chin, B., et al.: 3d segmentation in the clinic: A grand challenge ii: Ms lesion segmentation (11 2008)
2008
Earlier work this paper cites.
Saxe, A., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y.: On random weights and unsupervised feature learning. In: Getoor, L., Scheffer, T. (eds.) Proceedings of the 28th International Conference on Machine Learning (ICML-11). pp. 1089–1096. ACM, New York, NY, USA (2011)
2011
Earlier work this paper cites.
Ciresan, D., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images. In: NIPS 25, pp. 2843–2851. Curran Associates, Inc. (2012)
2012
Earlier work this paper cites.
Tieleman, T., Hinton, G.: Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning (2012)
2012
Earlier work this paper cites.
Liu, T., Jones, C., Seyedhosseini, M., Tasdizen, T.: A modular hierarchical approach to 3d electron microscopy image segmentation. Journal of Neuroscience Methods 226, 88 – 102 (2014)
2014
Earlier work this paper cites.
Menze, B., Jakab, A., Bauer, S., et al.: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE TMI p. 33 (2014)
2014
Earlier work this paper cites.
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. CoRR abs/1412.6550 (2014)
Original
2014
Earlier work this paper cites.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR abs/1409.4842 (2014)
Original
2014
Earlier work this paper cites.

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