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. 2019 Feb 6;9:1483. doi: 10.1038/s41598-018-37638-9

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© The Author(s) 2019

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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LAC tumor growth pattern classification performance in three independent sets by two models: (A) violin plots of F1-scores showing classification performance for acinar (AC), micropapillary (MP), solid (SO) and cribriform (CR) tumor growth patterns and non-tumor (NT) tissue in all test slides (n = 128). One data point represents one tissue class in one slide. (B) Violin plots of whole slide classification accuracy in slides from CSMC, MIMW and TCGA test sets. One data point represents one slide. C) Normalized study-level confusion matrices. Confusion matrices from CSMC, MIMW and TCGA test sets are shown in Supplementary Figure 2.