{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T10:09:19Z","timestamp":1747822159030,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685335"}],"license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":[[2024,8,22]]},"abstract":"<jats:p>Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs.<\/jats:p>","DOI":"10.3233\/shti240487","type":"book-chapter","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:34:23Z","timestamp":1724405663000},"source":"Crossref","is-referenced-by-count":1,"title":["Comparing nnU-Net and deepflash2 for Histopathological Tumor Segmentation"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6278-8759","authenticated-orcid":false,"given":"Daniel","family":"Hieber","sequence":"first","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences"},{"name":"Pathology, Medical Faculty, University of Augsburg"},{"name":"Institute of Medical Data Science, University Hospital W\u00fcrzburg"}]},{"given":"Nico","family":"Haisch","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science, Aalen University of Applied Sciences"}]},{"given":"Gregor","family":"Grambow","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science, Aalen University of Applied Sciences"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4020-9509","authenticated-orcid":false,"given":"Felix","family":"Holl","sequence":"additional","affiliation":[{"name":"DigiHealth Institute, Neu-Ulm University of Applied Sciences"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1948-1580","authenticated-orcid":false,"given":"Friederike","family":"Liesche-Starnecker","sequence":"additional","affiliation":[{"name":"Pathology, Medical Faculty, University of Augsburg"}]},{"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 W\u00fcrzburg"}]},{"given":"J\u00fcrgen","family":"Schlegel","sequence":"additional","affiliation":[{"name":"Pathology, Medical Faculty, University of Augsburg"},{"name":"Technical University of Munich"}]},{"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"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Digital Health and Informatics Innovations for Sustainable Health Care Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI240487","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T09:34:25Z","timestamp":1724405665000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI240487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"ISBN":["9781643685335"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti240487","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}