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Medientyp:
E-Artikel
Titel:
Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
Beteiligte:
Saldanha, Oliver Lester;
Loeffler, Chiara M. L.;
Niehues, Jan Moritz;
van Treeck, Marko;
Seraphin, Tobias P.;
Hewitt, Katherine Jane;
Cifci, Didem;
Veldhuizen, Gregory Patrick;
Ramesh, Siddhi;
Pearson, Alexander T.;
Kather, Jakob Nikolas
Erschienen:
Springer Science and Business Media LLC, 2023
Erschienen in:
npj Precision Oncology, 7 (2023) 1
Sprache:
Englisch
DOI:
10.1038/s41698-023-00365-0
ISSN:
2397-768X
Entstehung:
Anmerkungen:
Beschreibung:
AbstractThe 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.