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. 2022 Apr 20;2:45. doi: 10.1038/s43856-022-00107-6

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© The Author(s) 2022

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. 3 — a Example of a Region of Interest (ROI) tile, as the output of the ROI detection model, the You-Only-Look-Once (YOLO) cell detection and classification model architecture, and output examples of applying the YOLO model to detect and classify objects inside the input ROI tiles. b The cross-validation confusion matrix showing the performance of the YOLO cell detection and classification model applied on 16 different cytological and non-cytological object types representing 10 diagnostic categories. Each value represents the percentage of classification per object type across others. Rows indicate the ground-truth object class while columns display the object type predicted by the model. The diagonal values indicate the true positive portion for each object type and the other values, outside of the diagonal, display the misclassification rates. c Thumbnail images of 19 samples for cellular objects and non-cellular objects for cell detection and classification model.