Self-supervised Phenotype Learning
Self-Supervised Phenotype Learning is a computational framework within multimodal phenotyping that leverages unlabeled multimodal data to automatically discover and learn phenotypic representations by exploiting intrinsic data structures and cross-modal consistencies, without relying on predefined labels.
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Publications
627
Citations
93
Authors
40
Institutions
Publications per year
2017–2026
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University Hospital Heidelberg
Heidelberg, Germany
2 h-index
2 publications
92 citations
Heidelberg University
Heidelberg, Germany
2 h-index
2 publications
92 citations
Heinrich Heine University Düsseldorf
Düsseldorf, Germany
2 h-index
2 publications
92 citations
Technische Universität Dresden
Dresden, Germany
2 h-index
2 publications
92 citations
Düsseldorf University Hospital
Düsseldorf, Germany
2 h-index
2 publications
92 citations
University Hospital Carl Gustav Carus
Dresden, Germany
2 h-index
16 publications
736 citations
RWTH Aachen University
Aachen, Germany
2 h-index
16 publications
736 citations
University of Chicago
Chicago, United States
2 h-index
4 publications
184 citations
1–8 of 40
Repository · 2 publications · 59 citations
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Repository · 2 publications · 36 citations
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1 publications · 41 citations
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1 publications · 268 citations
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1 publications · 59 citations
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