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. 2020 Apr 3;10:5842. doi: 10.1038/s41598-020-62321-3

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Graphical illustration of our fully convolutional neural network architecture. Each block depicts a hidden layer of the network. The height of each of the blocks visualises the evolution of the spatial size in the network. As a comparison, the depth visualises the evolution of the number of the feature maps in the network. The number of the feature maps of each layer is also shown as a number on each block. The blocks containing concatenation are shown in dark grey. BN and ReLU correspond to the batch normalisation operation and the rectified linear unit non-linearity, respectively.