CMed: Crowd Analytics for Medical Imaging Data

Park, Ji Hwan; Nadeem, Saad; Boorboor, Saeed; Marino, Joseph; Kaufman, Arie

doi:10.1109/tvcg.2019.2953026

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Using C 2 A, doctors can discard most tumor-free video segments and focus on the ones that most likely to contain tumors. To analyze the accuracy of crowdsourcing workers, Park et al [34] developed CMed that visualizes clinical image annotations by crowdsourcing, and workers' behavior. By clustering workers according to their annotation accuracy and analyzing their logged events, experts are able to find good workers and observe the effects of workers' behavior patterns.…”

Section: Label-level Improvementmentioning

confidence: 99%

A survey of visual analytics techniques for machine learning

et al. 2020

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Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

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Section: Label-level Improvementmentioning

confidence: 99%

A survey of visual analytics techniques for machine learning

et al. 2020

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“…Before Model Building Improving Data Quality (31) [3], [11], [14], [16], [17], [18], [25], [45], [61], [91], [96], [101], [102], [118], [123], [125], [136], [144], [157], [193], [202], [204], [205], [214], [228], [229], [232], [257], [259], [268], [275] Improving Feature Quality (6) [109], [132], [184], [195], [223], [239] During Model Building Model Understanding (30) [28], [38], [56], [71], [79], [84], [104], [115], [116], [119], [120], [137],…”

Section: Technique Category Papers Trendmentioning

confidence: 99%

“…Using C 2 A, doctors can discard most tumor-free video segments and focus on the ones that most likely to contain tumors. To analyze the accuracy of crowdsourcing workers, Park et al [204] developed CMed that visualizes clinical image annotations by crowdsourcing, and workers' behavior. By clustering workers according to their annotation accuracy and analyzing their logged events, experts are able to find good workers and observe the effects of workers' behavior patterns.…”

Section: Label-level Improvementmentioning

confidence: 99%

A Survey of Visual Analytics Techniques for Machine Learning

Yuan

Chen

Yang

et al. 2020

Preprint

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Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during model building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

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