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. 2021 Nov 22;11:22636. doi: 10.1038/s41598-021-02168-4

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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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Visualization of the trained network. (a) An example of applying Grad- CAM++ to an H&E image (left) of an EBV gastric cancer. Grad- CAM++ (right) highlights the most discriminative area in this image to explain the decision made by the trained neural network (arrow). In a higher magnification (inset), intraepithelial lymphocytosis seems to be the most responsible focus for discriminating EBV + MSI/dMMR from others. Scale bar: 100 µm. (b) Created images with the highest probability to be predicted as “EBV + MSI/dMMR” (upper) and “others” (lower) with the method of activation maximization. In the former, the created image seems to represent intraepithelial lymphocytosis. EBV- Epstein-Barr virus, Grad-CAM-gradient-weighted class activation mapping, MSI- microsatellite instability, dMMR- mismatch repair deficiency.