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. 2021 Aug 4;11:15857. doi: 10.1038/s41598-021-94750-z

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© The Author(s) 2021

Open AccessThis 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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Synthetic radiographs, ground truth masks and results of reader and computer-based detection. (A) Synthetic input radiograph as shown to the reader and evaluated by the CNNs (B) corresponding ground-truth radiograph with nodules marked green (C) center position of nodules marked by a reader (D) U-Net prediction (E) RetinaNet bounding-box predictions with scores.