Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Springer International Publishing
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X Demographics
Mendeley readers
| Chapter title |
Unsupervised Contrastive Learning of Radiomics and Deep Features for Label-Efficient Tumor Classification
|
|---|---|
| Book title |
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
|
| Published by |
Springer, Cham, September 2021
|
| DOI | 10.1007/978-3-030-87196-3_24 |
| Book ISBNs |
978-3-03-087195-6, 978-3-03-087196-3
|
| Authors |
Zhao, Ziteng, Yang, Guanyu |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 33% |
| Lecturer > Senior Lecturer | 1 | 7% |
| Unknown | 9 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 33% |
| Physics and Astronomy | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Unknown | 8 | 53% |