Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task
Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm t...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
10 April 2019
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| In: |
European journal of cancer
Year: 2019, Jahrgang: 113, Pages: 47-54 |
| ISSN: | 1879-0852 |
| DOI: | 10.1016/j.ejca.2019.04.001 |
| Online-Zugang: | Resolving-System, Volltext: https://doi.org/10.1016/j.ejca.2019.04.001 Verlag: http://www.sciencedirect.com/science/article/pii/S0959804919302217 
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| Verfasserangaben: | Titus J. Brinker, Achim Hekler, Alexander H. Enk, Joachim Klode, Axel Hauschild, Carola Berking, Bastian Schilling, Sebastian Haferkamp, Dirk Schadendorf, Tim Holland-Letz, Jochen S. Utikal, Christof von Kalle, collaborators |
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| 245 | 1 | 0 | |a Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task |c Titus J. Brinker, Achim Hekler, Alexander H. Enk, Joachim Klode, Axel Hauschild, Carola Berking, Bastian Schilling, Sebastian Haferkamp, Dirk Schadendorf, Tim Holland-Letz, Jochen S. Utikal, Christof von Kalle, collaborators |
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| 520 | |a Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. | ||
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