| Title |
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
|
|---|---|
| Published in |
Journal of Digital Imaging, May 2019
|
| DOI | 10.1007/s10278-019-00227-x |
| Pubmed ID | |
| Authors |
Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul Kennedy |
X Demographics
The data shown below were collected from the profiles of 4 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 States | 3 | 75% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,381 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1381 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 220 | 16% |
| Student > Master | 200 | 14% |
| Researcher | 116 | 8% |
| Student > Bachelor | 99 | 7% |
| Student > Doctoral Student | 53 | 4% |
| Other | 174 | 13% |
| Unknown | 519 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 340 | 25% |
| Engineering | 234 | 17% |
| Medicine and Dentistry | 73 | 5% |
| Physics and Astronomy | 31 | 2% |
| Biochemistry, Genetics and Molecular Biology | 19 | 1% |
| Other | 122 | 9% |
| Unknown | 562 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 22. 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 30 August 2023.
All research outputs
#1,582,142
of 23,862,416 outputs
Outputs from Journal of Digital Imaging
#26
of 1,099 outputs
Outputs of similar age
#35,599
of 353,042 outputs
Outputs of similar age from Journal of Digital Imaging
#3
of 36 outputs
Altmetric has tracked 23,862,416 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,099 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 97% 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 353,042 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.