| Title |
Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features
|
|---|---|
| Published in |
European Radiology, May 2019
|
| DOI | 10.1007/s00330-019-06214-8 |
| Pubmed ID | |
| Authors |
Clinton J. Wang, Charlie A. Hamm, Lynn J. Savic, Marc Ferrante, Isabel Schobert, Todd Schlachter, MingDe Lin, Jeffrey C. Weinreb, James S. Duncan, Julius Chapiro, Brian Letzen |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 50% |
| Netherlands | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 172 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 172 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 14% |
| Researcher | 16 | 9% |
| Student > Master | 14 | 8% |
| Other | 13 | 8% |
| Student > Bachelor | 13 | 8% |
| Other | 31 | 18% |
| Unknown | 61 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 31 | 18% |
| Medicine and Dentistry | 28 | 16% |
| Engineering | 15 | 9% |
| Biochemistry, Genetics and Molecular Biology | 4 | 2% |
| Nursing and Health Professions | 4 | 2% |
| Other | 20 | 12% |
| Unknown | 70 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 27 August 2019.
All research outputs
#18,020,520
of 23,146,350 outputs
Outputs from European Radiology
#2,858
of 4,189 outputs
Outputs of similar age
#247,155
of 351,362 outputs
Outputs of similar age from European Radiology
#40
of 72 outputs
Altmetric has tracked 23,146,350 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,189 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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 351,362 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.