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Twitter Demographics
Mendeley readers
Chapter title |
A Deep Learning Approach to MR-less Spatial Normalization for Tau PET Images
|
---|---|
Chapter number | 40 |
Book title |
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
|
Published by |
Springer, Cham, October 2019
|
DOI | 10.1007/978-3-030-32245-8_40 |
Book ISBNs |
978-3-03-032244-1, 978-3-03-032245-8
|
Authors |
Jennifer Alvén, Kerstin Heurling, Ruben Smith, Olof Strandberg, Michael Schöll, Oskar Hansson, Fredrik Kahl, Alvén, J, Heurling, K, Smith, R, Strandberg, O, Schöll, M, Hansson, O, Kahl, F, Alvén, Jennifer, Heurling, Kerstin, Smith, Ruben, Strandberg, Olof, Schöll, Michael, Hansson, Oskar, Kahl, Fredrik |
Twitter Demographics
The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 22% |
Student > Master | 2 | 22% |
Student > Doctoral Student | 1 | 11% |
Lecturer | 1 | 11% |
Researcher | 1 | 11% |
Other | 0 | 0% |
Unknown | 2 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 2 | 22% |
Medicine and Dentistry | 2 | 22% |
Engineering | 1 | 11% |
Unknown | 4 | 44% |