| Title |
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
|
|---|---|
| Published in |
NeuroImage: Clinical, December 2019
|
| DOI | 10.1016/j.nicl.2019.102104 |
| Pubmed ID | |
| Authors |
Richard McKinley, Rik Wepfer, Lorenz Grunder, Fabian Aschwanden, Tim Fischer, Christoph Friedli, Raphaela Muri, Christian Rummel, Rajeev Verma, Christian Weisstanner, Benedikt Wiestler, Christoph Berger, Paul Eichinger, Mark Muhlau, Mauricio Reyes, Anke Salmen, Andrew Chan, Roland Wiest, Franca Wagner |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 5 | 71% |
| United States | 1 | 14% |
| Unknown | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 43% |
| Practitioners (doctors, other healthcare professionals) | 2 | 29% |
| Scientists | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 90 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 21% |
| Researcher | 11 | 12% |
| Student > Master | 11 | 12% |
| Student > Bachelor | 9 | 10% |
| Other | 4 | 4% |
| Other | 8 | 9% |
| Unknown | 28 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 20% |
| Medicine and Dentistry | 12 | 13% |
| Engineering | 10 | 11% |
| Neuroscience | 5 | 6% |
| Psychology | 1 | 1% |
| Other | 6 | 7% |
| Unknown | 38 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 January 2020.
All research outputs
#6,301,037
of 25,611,630 outputs
Outputs from NeuroImage: Clinical
#1,029
of 2,811 outputs
Outputs of similar age
#126,055
of 479,046 outputs
Outputs of similar age from NeuroImage: Clinical
#47
of 116 outputs
Altmetric has tracked 25,611,630 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,811 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has gotten more attention than average, scoring higher than 63% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 479,046 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.