| |  Online-Ressource |
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| Verfasst von: | Steinbuß, Georg [VerfasserIn]  |
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| | Kriegsmann, Mark [VerfasserIn] |
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| | Zgorzelski, Christiane [VerfasserIn] |
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| | Brobeil, Alexander [VerfasserIn] |
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| | Goeppert, Benjamin [VerfasserIn] |
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| | Dietrich, Sascha [VerfasserIn] |
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| | Mechtersheimer, Gunhild [VerfasserIn] |
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| | Kriegsmann, Katharina [VerfasserIn] |
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| Titel: | Deep learning for the classification of non-hodgkin lymphoma on histopathological images |
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| Verf.angabe: | Georg Steinbuss, Mark Kriegsmann, Christiane Zgorzelski, Alexander Brobeil, Benjamin Goeppert, Sascha Dietrich, Gunhild Mechtersheimer and Katharina Kriegsmann |
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| E-Jahr: | 2021 |
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| Jahr: | 17 May 2021 |
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| Umfang: | 11 S. |
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| Fussnoten: | Gesehen am 04.03.2022 |
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| Titel Quelle: | Enthalten in: Cancers |
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| Ort Quelle: | Basel : MDPI, 2009 |
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| Jahr Quelle: | 2021 |
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| Band/Heft Quelle: | 13(2021), 10, Artikel-ID 2419, Seite 1-11 |
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| ISSN Quelle: | 2072-6694 |
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| Abstract: | The diagnosis and the subtyping of non-Hodgkin lymphoma (NHL) are challenging and require expert knowledge, great experience, thorough morphological analysis, and often additional expensive immunohistological and molecular methods. As these requirements are not always available, supplemental methods supporting morphological-based decision making and potentially entity subtyping are required. Deep learning methods have been shown to classify histopathological images with high accuracy, but data on NHL subtyping are limited. After annotation of histopathological whole-slide images and image patch extraction, we trained and optimized an EfficientNet convolutional neuronal network algorithm on 84,139 image patches from 629 patients and evaluated its potential to classify tumor-free reference lymph nodes, nodal small lymphocytic lymphoma/chronic lymphocytic leukemia, and nodal diffuse large B-cell lymphoma. The optimized algorithm achieved an accuracy of 95.56% on an independent test set including 16,960 image patches from 125 patients after the application of quality controls. Automatic classification of NHL is possible with high accuracy using deep learning on histopathological images and routine diagnostic applications should be pursued. |
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| DOI: | doi:10.3390/cancers13102419 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext ; Verlag: https://doi.org/10.3390/cancers13102419 |
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| | Volltext: https://www.mdpi.com/2072-6694/13/10/2419 |
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| | DOI: https://doi.org/10.3390/cancers13102419 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | artificial intelligence |
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| | CLL/SLL |
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| | CNN |
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| | deep learning |
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| | DLBCL |
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| | histopathology |
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| K10plus-PPN: | 179468090X |
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| Verknüpfungen: | → Zeitschrift |
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Deep learning for the classification of non-hodgkin lymphoma on histopathological images / Steinbuß, Georg [VerfasserIn]; 17 May 2021 (Online-Ressource)
