Title |
Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
|
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Published in |
Ophthalmology Retina, January 2019
|
DOI | 10.1016/j.oret.2019.01.015 |
Pubmed ID | |
Authors |
Aaron S. Coyner, Ryan Swan, J. Peter Campbell, Susan Ostmo, James M. Brown, Jayashree Kalpathy-Cramer, Sang Jin Kim, Karyn E. Jonas, R.V. Paul Chan, Michael F. Chiang, Imaging and Informatics in Retinopathy of Prematurity Research Consortium, Michael F. Chiang, Susan Ostmo, Sang Jin Kim, Kemal Sonmez, J. Peter Campbell, R. V. Paul Chan, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Jayashree Kalpathy-Cramer, Ken Chang, Andrew Beers, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, Cristina Montero-Mendoza |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 50% |
Unknown | 6 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 50% |
Scientists | 3 | 25% |
Practitioners (doctors, other healthcare professionals) | 2 | 17% |
Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 73 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 15% |
Student > Postgraduate | 7 | 10% |
Student > Master | 6 | 8% |
Student > Doctoral Student | 6 | 8% |
Researcher | 5 | 7% |
Other | 12 | 16% |
Unknown | 26 | 36% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 16 | 22% |
Computer Science | 16 | 22% |
Engineering | 5 | 7% |
Nursing and Health Professions | 2 | 3% |
Mathematics | 1 | 1% |
Other | 5 | 7% |
Unknown | 28 | 38% |