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. 2020 Feb 11;10:2362. doi: 10.1038/s41598-020-59276-w

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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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Feature importance and the mean decrease in the Gini index. Features are colored by type and may be calculated from three different sources: pixel intensities, segmentation-derived binary masks (i.e. images encoded as matrices in which identified objects are represented by 1, while background is coded as 0), and morphological top-hat transformations of pixel intensities.