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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050159/ https://www.ncbi.nlm.nih.gov/pubmed/36977919 http://dx.doi.org/10.1038/s41698-023-00365-0 |
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author | 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 |
author_facet | 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 |
author_sort | Saldanha, Oliver Lester |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10050159 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100501592023-03-30 Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology 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 NPJ Precis Oncol Brief Communication 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. Nature Publishing Group UK 2023-03-28 /pmc/articles/PMC10050159/ /pubmed/36977919 http://dx.doi.org/10.1038/s41698-023-00365-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Brief Communication 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 Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
title | Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
title_full | Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
title_fullStr | Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
title_full_unstemmed | Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
title_short | Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
title_sort | self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology |
topic | Brief Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050159/ https://www.ncbi.nlm.nih.gov/pubmed/36977919 http://dx.doi.org/10.1038/s41698-023-00365-0 |
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