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We constructed a graph from segmented regions of WSI, where nodes represented segmented pathological features and edges represented spatial relationships. The model was trained on a dataset of 422 cases from the Shandong Provincial Center for Disease Control and Prevention, annotated for tuberculosis, brucellosis, and purulent spondylitis. Performance metrics included accuracy, precision, recall, and F1 score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The integrated GCN model demonstrated a classification accuracy of 87%, with a recall of 85% and an F1 score of 0.86. These metrics signify an improvement over traditional CNN models, which showed a 10% lower performance in comparative analyses. The model also effectively quantified uncertainty, enhancing confidence in diagnostic decisions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Integrating GCNs with model uncertainty modeling enhances the accuracy and reliability of WSI image classification in pathology. This approach significantly improves the capture of spatial relationships and pathological feature recognition, offering a robust framework for supporting diagnostic and therapeutic decisions in medical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance<\/jats:title>\n                    <jats:p>The enhanced ability to classify and understand WSI images using this method has significant implications for pathology, potentially leading to more accurate and reliable diagnoses. This approach could be particularly useful in remote diagnostics and in environments where expert pathological consultation is limited.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-025-02130-0","type":"journal-article","created":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T13:04:13Z","timestamp":1769864653000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing WSI image classification with graph convolutional neural networks and model uncertainty modeling"],"prefix":"10.1186","volume":"26","author":[{"given":"Chaoyue","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxiang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanke","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,31]]},"reference":[{"key":"2130_CR1","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","volume":"30","author":"MY Lu","year":"2024","unstructured":"Lu MY, et al. 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The Committee is independent of the hospital and the sponsor and operates in accordance with ICH-GCP\/China GCP and relevant regulations, including the Administrative Measures for Ethical Review of Life Sciences and Medical Research Involving Humans (2023, National Health Commission), NMPA Good Clinical Practice for Drugs (2020), Good Clinical Practice for Medical Devices (2022), as well as the WMA Declaration of Helsinki (2013) and the CIOMS International Ethical Guidelines. Because this was a retrospective analysis of de-identified clinical pathology data, the requirement for informed consent was waived by the Committee. All data were anonymized prior to analysis; no identifiable personal information or images are included.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors have obtained consent for publication from all participants involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"112"}}