A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma: original article
To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC).
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
August 2023
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| In: |
World journal of urology
Year: 2023, Volume: 41, Issue: 8, Pages: 2233-2241 |
| ISSN: | 1433-8726 |
| DOI: | 10.1007/s00345-023-04489-7 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00345-023-04489-7 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s00345-023-04489-7 
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| Author Notes: | Frederik Wessels, Max Schmitt, Eva Krieghoff-Henning, Malin Nientiedt, Frank Waldbillig, Manuel Neuberger, Maximilian C. Kriegmair, Karl-Friedrich Kowalewski, Thomas S. Worst, Matthias Steeg, Zoran V. Popovic, Timo Gaiser, Christof von Kalle, Jochen S. Utikal, Stefan Fröhling, Maurice S. Michel, Philipp Nuhn, Titus J. Brinker |
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| 520 | |a To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). | ||
| 650 | 4 | |a Artificial intelligence | |
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| 650 | 4 | |a Risk assessment | |
| 650 | 4 | |a Survival analysis | |
| 650 | 4 | |a Treatment outcome | |
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