| Title |
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
|
|---|---|
| Published in |
bioRxiv, December 2017
|
| DOI | 10.1101/234120 |
| Authors |
Vladimir Iglovikov, Alexander Rakhlin, Alexandr Kalinin, Alexey Shvets |
X Demographics
The data shown below were collected from the profiles of 211 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 54 | 26% |
| India | 13 | 6% |
| United Kingdom | 6 | 3% |
| Spain | 4 | 2% |
| Canada | 3 | 1% |
| Australia | 3 | 1% |
| Greece | 3 | 1% |
| Venezuela, Bolivarian Republic of | 3 | 1% |
| Japan | 3 | 1% |
| Other | 35 | 17% |
| Unknown | 84 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 183 | 87% |
| Scientists | 18 | 9% |
| Practitioners (doctors, other healthcare professionals) | 9 | 4% |
| Science communicators (journalists, bloggers, editors) | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 19% |
| Researcher | 5 | 16% |
| Student > Bachelor | 4 | 13% |
| Student > Ph. D. Student | 4 | 13% |
| Professor | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 8 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 32% |
| Engineering | 7 | 23% |
| Physics and Astronomy | 2 | 6% |
| Medicine and Dentistry | 2 | 6% |
| Neuroscience | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 9 | 29% |