Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance.

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Bibliographic Details
Title: Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance.
Authors: Niehues JM; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany., Müller-Franzes G; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Schirris Y; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Netherlands Cancer Institute, 1066 CX, Amsterdam, the Netherlands; University of Amsterdam, 1012 WP, Amsterdam, the Netherlands., Wagner SJ; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Helmholtz Munich - German Research Center for Environment and Health, Munich, Germany; School of Computation, Information and Technology, Technical University of Munich, Munich, Germany., Jendrusch M; Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany., Kloor M; Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany., Pearson AT; Department of Medicine, University of Chicago, Chicago, USA., Muti HS; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany., Hewitt KJ; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany., Veldhuizen GP; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany., Zigutyte L; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany., Truhn D; Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany., Kather JN; Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Pathology & Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Department of Medicine I, University Hospital Dresden, Dresden, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. Electronic address: jakob-nikolas.kather@alumni.dkfz.de.
Source: Computers in biology and medicine [Comput Biol Med] 2024 Jun; Vol. 175, pp. 108410. Date of Electronic Publication: 2024 Apr 04.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-0534
DOI:10.1016/j.compbiomed.2024.108410