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. 2021 Sep 24;12:5639. doi: 10.1038/s41467-021-25296-x

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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 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. 1 — Our system consists of WSI redundant division, LR model, HR model, and RNN model. The LR model takes a divided image tile of 512 × 512 pixels (0.486 μm/pixel) as input and outputs a lesion probability and a location heatmap to identify and locate the suspicious lesion areas on WSIs. The HR model takes an image tile of 256 × 256 (0.243 μm/pixel) cropped according to the location heatmap as input and outputs a new lesion probability. The RNN model integrates the HR model image features of the top 10 lesion cells and outputs positive probabilities of WSIs. The clinical dataset images in this figure were created by us.