{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:58:24Z","timestamp":1761901104066,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686158"}],"license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,3]]},"abstract":"<jats:p>Introduction: Mitotic figure (MF) density has been established as a key biomarker for certain tumors. Recently, the differentiation between atypical MFs (AMF) and normal MFs (NMFs) has gained increased interest in research, as AMFs density could be an independent biomarker. This results in the challenge of finding an automated, deterministic way to differentiate between AMFs and NMFs. Methods: In this study, the AUCMEDI deep learning framework is applied to the recently published AMi-Br dataset to get a first bearing on the complexity of the task at hand. The dataset includes eight mitotic subclasses derived from breast cancer samples, four for NMFs and four for AMF. Using a patient-level cross-validation strategy and a ConvNeXt-based ensemble, we trained and evaluated an eight-class subtype classification model. Results: Our results show high specificity across all classes (\u2265 90%), but sensitivity varies significantly between mitotic subclasses (0\u201382%), reflecting the dataset\u2019s inherent challenges. The mean AUC of 85.90% outperforms the binary classification baseline (69.8%). Conclusion: The results highlight the promise of progress in subclass-level mitotic analysis while pointing to areas for further model refinement.<\/jats:p>","DOI":"10.3233\/shti251413","type":"book-chapter","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T10:25:07Z","timestamp":1756895107000},"source":"Crossref","is-referenced-by-count":0,"title":["Classifying the AMi-Br Mitotic Figure Dataset with AUCMEDI"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6278-8759","authenticated-orcid":false,"given":"Daniel","family":"Hieber","sequence":"first","affiliation":[{"name":"Department of Neuropathology, Pathology, Medical Faculty, University of Augsburg"},{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences"},{"name":"Institute of Medical Data Science, University Hospital of W\u00fcrzburg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1948-1580","authenticated-orcid":false,"given":"Friederike","family":"Liesche-Starnecker","sequence":"additional","affiliation":[{"name":"Department of Neuropathology, Pathology, Medical Faculty, University of Augsburg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6874-9478","authenticated-orcid":false,"given":"Johannes","family":"Schobel","sequence":"additional","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1522-785X","authenticated-orcid":false,"given":"R\u00fcdiger","family":"Pryss","sequence":"additional","affiliation":[{"name":"Institute of Medical Data Science, University Hospital of W\u00fcrzburg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2857-7122","authenticated-orcid":false,"given":"Frank","family":"Kramer","sequence":"additional","affiliation":[{"name":"Faculty of Applied Computer Science, University of Augsburg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0838-9885","authenticated-orcid":false,"given":"Dominik","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Faculty of Applied Computer Science, University of Augsburg"},{"name":"Institute for Digital Medicine, University Hospital Augsburg"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences 2025: GMDS Illuminates Health"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251413","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:38:02Z","timestamp":1761899882000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251413"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"ISBN":["9781643686158"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251413","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,9,3]]}}}