Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.

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Bibliographic Details
Title: Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.
Authors: Müller D; Faculty of Applied Computer Science, University of Augsburg, Germany.; Institute for Digital Medicine, University Hospital Augsburg, Germany., Meyer P; Institute for Digital Medicine, University Hospital Augsburg, Germany., Rentschler L; Institute for Digital Medicine, University Hospital Augsburg, Germany.; Institute for Pathology, University Hospital Augsburg, Germany., Manz R; Institute for Digital Medicine, University Hospital Augsburg, Germany., Hieber D; Institute for Pathology, University Hospital Augsburg, Germany.; Institute DigiHealth, Neu-Ulm University of Applied Sciences, Germany.; Bavarian Cancer Research Center (BZKF), Augsburg, Germany., Bäcker J; Institute for Digital Medicine, University Hospital Augsburg, Germany., Cramer S; Institute for Digital Medicine, University Hospital Augsburg, Germany., Wengenmayr C; Institute for Digital Medicine, University Hospital Augsburg, Germany., Märkl B; Institute for Pathology, University Hospital Augsburg, Germany., Huss R; Institute for Pathology, University Hospital Augsburg, Germany.; BioM Biotech Cluster Development GmbH, Germany., Kramer F; Faculty of Applied Computer Science, University of Augsburg, Germany., Soto-Rey I; Institute for Digital Medicine, University Hospital Augsburg, Germany., Raffler J; Institute for Digital Medicine, University Hospital Augsburg, Germany.; Bavarian Cancer Research Center (BZKF), Augsburg, Germany.
Source: Studies in health technology and informatics [Stud Health Technol Inform] 2024 Aug 22; Vol. 316, pp. 1110-1114.
Publication Type: Journal Article
Journal Info: Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-8365
DOI:10.3233/SHTI240605