Title |
Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset
|
---|---|
Published in |
Journal of Digital Imaging, October 2018
|
DOI | 10.1007/s10278-018-0144-1 |
Pubmed ID | |
Authors |
Richard Ha, Christine Chin, Jenika Karcich, Michael Z. Liu, Peter Chang, Simukayi Mutasa, Eduardo Pascual Van Sant, Ralph T. Wynn, Eileen Connolly, Sachin Jambawalikar |
X Demographics
The data shown below were collected from the profiles of 9 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 | 2 | 22% |
Switzerland | 1 | 11% |
Germany | 1 | 11% |
Thailand | 1 | 11% |
Netherlands | 1 | 11% |
Unknown | 3 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 4 | 44% |
Scientists | 2 | 22% |
Members of the public | 2 | 22% |
Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 144 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 20 | 14% |
Student > Master | 16 | 11% |
Student > Bachelor | 16 | 11% |
Researcher | 13 | 9% |
Student > Doctoral Student | 5 | 3% |
Other | 20 | 14% |
Unknown | 54 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 25 | 17% |
Computer Science | 20 | 14% |
Engineering | 17 | 12% |
Biochemistry, Genetics and Molecular Biology | 4 | 3% |
Agricultural and Biological Sciences | 3 | 2% |
Other | 9 | 6% |
Unknown | 66 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 20 June 2019.
All research outputs
#2,221,025
of 23,108,064 outputs
Outputs from Journal of Digital Imaging
#58
of 1,067 outputs
Outputs of similar age
#49,504
of 350,226 outputs
Outputs of similar age from Journal of Digital Imaging
#5
of 37 outputs
Altmetric has tracked 23,108,064 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,067 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 94% 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 350,226 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 85% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.