Name
Abstract | ||
|---|---|---|
We present a visual analytics framework, CMed, for exploring medical image data annotations acquired from crowdsourcing. CMed can be used to visualize, classify, and filter crowdsourced clinical data based on a number of different metrics such as detection rate, logged events, and clustering of the annotations. CMed provides several interactive linked visualization components to analyze the crowd ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TVCG.2019.2953026 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | DocType | Volume |
Crowdsourcing,Biomedical imaging,Data visualization,Task analysis,Visual analytics,Lung,Computed tomography | Journal | 27 |
Issue | ISSN | Citations |
6 | 1077-2626 | 1 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ji hwan Park | 1 | 19 | 7.05 |
| Saad Nadeem | 2 | 18 | 6.83 |
| Saeed Boorboor | 3 | 1 | 1.35 |
| Joseph Marino | 4 | 70 | 11.35 |
| Arie Kaufman | 5 | 33 | 4.13 |