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| Chapter title |
Automated Detection of New or Evolving Melanocytic Lesions Using a 3D Body Model
|
|---|---|
| Chapter number | 74 |
| Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
|
| Published in |
Lecture notes in computer science, September 2014
|
| DOI | 10.1007/978-3-319-10404-1_74 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-910403-4, 978-3-31-910404-1
|
| Authors |
Federica Bogo, Javier Romero, Enoch Peserico, Michael J. Black |
| Editors |
Polina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 35 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 38% |
| Other | 7 | 19% |
| Researcher | 5 | 14% |
| Student > Master | 3 | 8% |
| Professor | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 4 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 24% |
| Engineering | 7 | 19% |
| Medicine and Dentistry | 6 | 16% |
| Mathematics | 2 | 5% |
| Biochemistry, Genetics and Molecular Biology | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 8 | 22% |