You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
| Chapter title |
Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth
|
|---|---|
| Chapter number | 16 |
| Book title |
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
|
| Published by |
Springer, Cham, October 2020
|
| DOI | 10.1007/978-3-030-60365-6_16 |
| Book ISBNs |
978-3-03-060364-9, 978-3-03-060365-6
|
| Authors |
Hassna Irzan, Lucas Fidon, Tom Vercauteren, Sebastien Ourselin, Neil Marlow, Andrew Melbourne, Irzan, Hassna, Fidon, Lucas, Vercauteren, Tom, Ourselin, Sebastien, Marlow, Neil, Melbourne, Andrew |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 1 | 10% |
| Student > Doctoral Student | 1 | 10% |
| Professor | 1 | 10% |
| Student > Ph. D. Student | 1 | 10% |
| Student > Master | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 4 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 1 | 10% |
| Computer Science | 1 | 10% |
| Neuroscience | 1 | 10% |
| Medicine and Dentistry | 1 | 10% |
| Engineering | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 5 | 50% |