| Title |
Collaborative and Reproducible Research: Goals, Challenges, and Strategies
|
|---|---|
| Published in |
Journal of Digital Imaging, February 2018
|
| DOI | 10.1007/s10278-017-0043-x |
| Pubmed ID | |
| Authors |
Steve G. Langer, George Shih, Paul Nagy, Bennet A. Landman |
| Abstract |
Combining imaging biomarkers with genomic and clinical phenotype data is the foundation of precision medicine research efforts. Yet, biomedical imaging research requires unique infrastructure compared with principally text-driven clinical electronic medical record (EMR) data. The issues are related to the binary nature of the file format and transport mechanism for medical images as well as the post-processing image segmentation and registration needed to combine anatomical and physiological imaging data sources. The SiiM Machine Learning Committee was formed to analyze the gaps and challenges surrounding research into machine learning in medical imaging and to find ways to mitigate these issues. At the 2017 annual meeting, a whiteboard session was held to rank the most pressing issues and develop strategies to meet them. The results, and further reflections, are summarized in this paper. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 40% |
| Netherlands | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 3 | 60% |
| Members of the public | 2 | 40% |
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 % |
|---|---|---|
| Researcher | 6 | 16% |
| Other | 5 | 14% |
| Student > Ph. D. Student | 5 | 14% |
| Student > Bachelor | 4 | 11% |
| Student > Master | 3 | 8% |
| Other | 3 | 8% |
| Unknown | 11 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 27% |
| Computer Science | 7 | 19% |
| Engineering | 3 | 8% |
| Business, Management and Accounting | 1 | 3% |
| Nursing and Health Professions | 1 | 3% |
| Other | 4 | 11% |
| Unknown | 11 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 08 August 2019.
All research outputs
#13,624,504
of 23,504,791 outputs
Outputs from Journal of Digital Imaging
#614
of 1,088 outputs
Outputs of similar age
#168,176
of 331,412 outputs
Outputs of similar age from Journal of Digital Imaging
#10
of 19 outputs
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So far Altmetric has tracked 1,088 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.