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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images
|
|---|---|
| Published in |
Frontiers in oncology, January 2021
|
| DOI | 10.3389/fonc.2020.619803 |
| Pubmed ID | |
| Authors |
Min Seob Kwak, Hun Hee Lee, Jae Min Yang, Jae Myung Cha, Jung Won Jeon, Jin Young Yoon, Ha Il Kim |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 7% |
| Switzerland | 1 | 7% |
| Unknown | 12 | 86% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 79% |
| Science communicators (journalists, bloggers, editors) | 2 | 14% |
| Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 16% |
| Researcher | 5 | 14% |
| Student > Master | 3 | 8% |
| Unspecified | 2 | 5% |
| Other | 2 | 5% |
| Other | 2 | 5% |
| Unknown | 17 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 24% |
| Biochemistry, Genetics and Molecular Biology | 3 | 8% |
| Computer Science | 2 | 5% |
| Unspecified | 2 | 5% |
| Engineering | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 16 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 May 2022.
All research outputs
#5,424,238
of 25,872,466 outputs
Outputs from Frontiers in oncology
#1,912
of 22,820 outputs
Outputs of similar age
#141,198
of 534,140 outputs
Outputs of similar age from Frontiers in oncology
#79
of 707 outputs
Altmetric has tracked 25,872,466 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 22,820 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done particularly well, scoring higher than 91% 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 534,140 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 707 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.