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. 2020 May 22;3:76. doi: 10.1038/s41746-020-0282-y

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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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Fig. 5 — Diagram detailing the network architecture for both a the image enhancement network and b the semantic segmentation network. The numbers above the layers represent the size of the tensor dimensions at that point in the network, in the format: length × width × number of channels. N was chosen to be 128 during training and can be set to any arbitrary size during testing. Scale bar indicates 20 μm.