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. 2022 Nov 8;13:6753. doi: 10.1038/s41467-022-34275-9

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© The Author(s) 2022

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 — a A 2D visualization of the image feature vectors by applying t-SNE. Each point represents a single patient in the BCCA test set. The t-SNE embedding maps patients with similar image features to near points, and patients with dissimilar image features to far points. The points are colored by the PD-L1 prediction scores of their corresponding patients. The 8 patients that were classified positive by the first pathologist and low-PS by the system are marked and their classifications by both pathologists are noted. b The TMA images corresponding to the t-SNE embedding are presented. Several examples of low and high prediction score images are shown, to demonstrate the characteristics observed by the pathologists. Examples of partially missing tissues are shown at the bottom.