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Title:

Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy

Document type:
Zeitschriftenaufsatz
Author(s):
Holmberg, Olle G.; Köhler, Niklas D.; Martins, Thiago; Siedlecki, Jakob; Herold, Tina; Keidel, Leonie; Asani, Ben; Schiefelbein, Johannes; Priglinger, Siegfried; Kortuem, Karsten U.; Theis, Fabian J.
Journal title:
Nature Machine Intelligence
Year:
2020
Journal volume:
2
Journal issue:
11
Pages contribution:
719-726
Fulltext / DOI:
doi:10.1038/s42256-020-00247-1
Publisher:
Springer Science and Business Media LLC
E-ISSN:
2522-5839
Date of publication:
01.11.2020
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