| Title |
Toolkits and Libraries for Deep Learning
|
|---|---|
| Published in |
Journal of Digital Imaging, March 2017
|
| DOI | 10.1007/s10278-017-9965-6 |
| Pubmed ID | |
| Authors |
Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick |
| Abstract |
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images. |
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 % |
|---|---|---|
| United States | 2 | 29% |
| Turkey | 2 | 29% |
| Germany | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 57% |
| Scientists | 2 | 29% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 343 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Unknown | 342 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 50 | 15% |
| Student > Master | 49 | 14% |
| Researcher | 35 | 10% |
| Student > Bachelor | 28 | 8% |
| Other | 17 | 5% |
| Other | 70 | 20% |
| Unknown | 94 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 93 | 27% |
| Medicine and Dentistry | 43 | 13% |
| Engineering | 41 | 12% |
| Biochemistry, Genetics and Molecular Biology | 11 | 3% |
| Agricultural and Biological Sciences | 10 | 3% |
| Other | 43 | 13% |
| Unknown | 102 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 13 February 2019.
All research outputs
#8,022,012
of 24,998,746 outputs
Outputs from Journal of Digital Imaging
#351
of 1,122 outputs
Outputs of similar age
#128,387
of 339,758 outputs
Outputs of similar age from Journal of Digital Imaging
#8
of 14 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 68% 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 339,758 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 61% of its contemporaries.
We're also able to compare this research output to 14 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 50% of its contemporaries.