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. 2023 Jan 3;14:100183. doi: 10.1016/j.jpi.2022.100183

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© 2022 The Authors

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Fig. 4 — The color heterogeneity among medical sources. The figure shows the distribution of the 6-dimensional H&E matrix, projected in a bi-dimensional space using the PCA algorithm. In the first row, colon data are analyzed. The left part of the row (Colon datasets) shows the distribution of color variations across colon datasets, while the right part of the row shows the distribution of color variations across training, validation, and testing for colon data (Colon partitions). In the second row, prostate data are analyzed. The left part of the row (Prostate datasets) shows the distribution of color variations across prostate datasets, while the right part of the row shows the distribution of color variations across training, validation, and testing for prostate data (Prostate partitions). In the third row, data collected to build the color variation database are analyzed. The left part of the row shows the distribution of data across different sources (Database sources), while the right part shows the distribution of the database compared with the distributions of colon and prostate use cases (Database and use cases data).