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. 2024 Jul 19;8:151. doi: 10.1038/s41698-024-00652-4

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© The Author(s) 2024

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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Fig. 1 — a The patches are first extracted from the WSIs in both the source and target domains. b Through FFT-Enhancer, patch colors from the source domain are adjusted to look more like patches from the target domain. c The label predictor is trained using features derived from the source domain, whereas the domain classifier is optimized using features derived from both the source and target domains. d In order to predict slide-level labels, the extracted features are fed into the VLAD aggregation method.