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. 2020 Jun 23;10:10161. doi: 10.1038/s41598-020-67052-z

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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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Analytical results of label-free digital pathology for colorectal cancer (CRC) diagnostics. In (A), the random forest (RF) classifier structure and the corresponding color code are shown schematically. The first RF (first row) determines healthy and pathologic regions, the second RF (second row) further classifies the pathologic regions to identify cancer regions, and the third RF (third row) determines microsatellite status of cancer regions. In (B) and (C), the resulting infrared (IR) index color images are shown for a microsatellite stable (MSS) CRC (B) and high microsatellite instable (MSI-H) CRC (C).