Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of ge...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
28 March 2023
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| In: |
npj precision oncology
Year: 2023, Jahrgang: 7, Pages: 1-5 |
| ISSN: | 2397-768X |
| DOI: | 10.1038/s41698-023-00365-0 |
| Online-Zugang: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1038/s41698-023-00365-0 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41698-023-00365-0 
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| Verfasserangaben: | Oliver Lester Saldanha, Chiara M. L. Loeffler, Jan Moritz Niehues, Marko van Treeck, Tobias P. Seraphin, Katherine Jane Hewitt, Didem Cifci, Gregory Patrick Veldhuizen, Siddhi Ramesh, Alexander T. Pearson and Jakob Nikolas Kather |
| Zusammenfassung: | The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability. |
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| Beschreibung: | Gesehen am 6.11.2023 |
| Beschreibung: | Online Resource |
| ISSN: | 2397-768X |
| DOI: | 10.1038/s41698-023-00365-0 |