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|a 10.1109/TVCG.2019.2953026
|2 doi
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|a eng
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1 |
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|a Park, Ji Hwan
|e verfasserin
|4 aut
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| 245 |
1 |
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|a CMed
|b Crowd Analytics for Medical Imaging Data
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|c 2021
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|a Text
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|a Date Completed 11.01.2022
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|a Date Revised 11.01.2022
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|a published: Print-Electronic
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| 500 |
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|a Citation Status MEDLINE
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| 520 |
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|a 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 annotation results for a particular video and the associated workers. Additionally, all results of an individual worker can be inspected using multiple linked views in our CMed framework. We allow a crowdsourcing application analyst to observe patterns and gather insights into the crowdsourced medical data, helping him/her design future crowdsourcing applications for optimal output from the workers. We demonstrate the efficacy of our framework with two medical crowdsourcing studies: polyp detection in virtual colonoscopy videos and lung nodule detection in CT thin-slab maximum intensity projection videos. We also provide experts' feedback to show the effectiveness of our framework. Lastly, we share the lessons we learned from our framework with suggestions for integrating our framework into a clinical workflow
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|a Journal Article
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1 |
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|a Nadeem, Saad
|e verfasserin
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| 700 |
1 |
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|a Boorboor, Saeed
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Marino, Joseph
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kaufman, Arie
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on visualization and computer graphics
|d 1996
|g 27(2021), 6 vom: 20. Juni, Seite 2869-2880
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|g year:2021
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|g day:20
|g month:06
|g pages:2869-2880
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|u http://dx.doi.org/10.1109/TVCG.2019.2953026
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