Deep Learning-based Phenotyping
Deep Learning-Based Phenotyping is an imaging-based phenotyping approach that leverages deep neural networks to automatically extract and classify phenotypic traits from high-dimensional image data, distinguishing itself from conventional methods through its capacity for hierarchical feature learning and end-to-end model optimization.
349
Publications
20.2K
Citations
2.5K
Authors
1K
Institutions
Publications per year
2017–2026
332
Parents
349
Identifying facial phenotypes of genetic disorders using deep learning
Yaron Gurovich, Yair Hanani, Omri Bar et al. · Nature Medicine · 2018 · 732 citations
Risk Prediction with Electronic Health Records: A Deep Learning Approach
Yu Cheng, Fei Wang, Ping Zhang et al. · 2016 · 400 citations
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Stanford University
Stanford, United States
13 h-index
46 publications
4.1K citations
Harvard University
Cambridge, United States
12 h-index
39 publications
4.5K citations
Massachusetts General Hospital
Boston, United States
11 h-index
33 publications
3.5K citations
University of Oxford
Oxford, United Kingdom
11 h-index
35 publications
2.7K citations
University of California, San Francisco
San Francisco, United States
11 h-index
37 publications
2.2K citations
University of California, Los Angeles
Los Angeles, United States
9 h-index
19 publications
736 citations

Johns Hopkins University
Baltimore, United States
9 h-index
10 publications
708 citations

University of Nottingham
Nottingham, United Kingdom
8 h-index
38 publications
5.9K citations
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15 publications · 1.1K citations
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Repository · 17 publications · 484 citations
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13 publications · 1.5K citations
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10 publications · 833 citations
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9 publications · 371 citations
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