| Title |
A Decision-Support Tool for Renal Mass Classification
|
|---|---|
| Published in |
Journal of Digital Imaging, July 2018
|
| DOI | 10.1007/s10278-018-0100-0 |
| Pubmed ID | |
| Authors |
Gautam Kunapuli, Bino A. Varghese, Priya Ganapathy, Bhushan Desai, Steven Cen, Manju Aron, Inderbir Gill, Vinay Duddalwar |
| Abstract |
We investigate the viability of statistical relational machine learning algorithms for the task of identifying malignancy of renal masses using radiomics-based imaging features. Features characterizing the texture, signal intensity, and other relevant metrics of the renal mass were extracted from multiphase contrast-enhanced computed tomography images. The recently developed formalism of relational functional gradient boosting (RFGB) was used to learn human-interpretable models for classification. Experimental results demonstrate that RFGB outperforms many standard machine learning approaches as well as the current diagnostic gold standard of visual qualification by radiologists. |
X Demographics
The data shown below were collected from the profiles of 3 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 | 1 | 33% |
| Netherlands | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 66 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 14% |
| Student > Master | 9 | 14% |
| Researcher | 8 | 12% |
| Student > Doctoral Student | 6 | 9% |
| Student > Bachelor | 3 | 5% |
| Other | 10 | 15% |
| Unknown | 21 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 27% |
| Computer Science | 7 | 11% |
| Engineering | 4 | 6% |
| Business, Management and Accounting | 2 | 3% |
| Unspecified | 2 | 3% |
| Other | 6 | 9% |
| Unknown | 27 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 07 November 2019.
All research outputs
#15,539,088
of 23,094,276 outputs
Outputs from Journal of Digital Imaging
#737
of 1,067 outputs
Outputs of similar age
#209,171
of 327,716 outputs
Outputs of similar age from Journal of Digital Imaging
#16
of 21 outputs
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So far Altmetric has tracked 1,067 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.