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. 2021 Apr 16;11:8381. doi: 10.1038/s41598-021-87737-3

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© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Grad-CAM results of the cannulation difficulty prediction model for accurately predicted examples. The heatmap visualizations are the outputs of Grad-CAM for the written GT label. They show where the model focused attention to estimate the cannulation difficulty. The lighter the color, the stronger is the attention required. The results show that the model sees distinct features of each label.