{"id":2173,"date":"2024-09-26T23:09:05","date_gmt":"2024-09-26T23:09:05","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/?post_type=qsc_member&p=2173"},"modified":"2024-09-26T23:09:05","modified_gmt":"2024-09-26T23:09:05","slug":"catherine-austin","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/catherine-austin\/","title":{"rendered":"Catherine Austin"},"content":{"rendered":"
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Catherine Austin<\/h1><\/div>\n\t\t
Undergraduate Student<\/div>\n\t\t
School of Computing<\/div>\n\t\t
Queen’s University<\/div>\n\t\t
Member from 2021<\/em> to present<\/em><\/div>\n\t\t
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19cma6@queensu.ca<\/a><\/div>\n\t\t\t
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Biography<\/h2>\n\n\n\n

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Publications<\/h2>\n\n\n
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Austin, Catherine; Hisey, Rebecca; O'Driscoll, Olivia; Ungi, Tamas; Fichtinger, Gabor<\/p>

Using uncertainty quantification to improve reliability of video-based skill assessment metrics in central venous catheterization<\/a> Journal Article<\/span> <\/p>

In: <\/span>vol. 12466, <\/span>pp. 84-88, <\/span>2023<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{fichtinger2023y,
\r\ntitle = {Using uncertainty quantification to improve reliability of video-based skill assessment metrics in central venous catheterization},
\r\nauthor = {Catherine Austin and Rebecca Hisey and Olivia O'Driscoll and Tamas Ungi and Gabor Fichtinger},
\r\nurl = {https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12466\/124660C\/Using-uncertainty-quantification-to-improve-reliability-of-video-based-skill\/10.1117\/12.2654419.short},
\r\nyear  = {2023},
\r\ndate = {2023-01-01},
\r\nvolume = {12466},
\r\npages = {84-88},
\r\npublisher = {SPIE},
\r\nabstract = {Computed-based skill assessment relies on accurate metrics to provide comprehensive feedback to trainees. Improving the accuracy of video-based metrics computed using object detection is generally done by improving the performance of the object detection network, however increasing its performance requires resources that cannot always be obtained. This study aims to improve the accuracy of metrics in central venous catheterization without requiring a high performing object detection network by removing false positive predictions identified using uncertainty quantification. The uncertainty for each bounding box was calculated using an entropy equation. The uncertainties were then compared to an uncertainty threshold computed using the optimal point of a Receiver Operating Characteristic curve. Predictions were removed if the uncertainty fell below the predefined threshold. 50 videos were recorded and \u2026},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
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Close<\/a><\/p><\/div>
Computed-based skill assessment relies on accurate metrics to provide comprehensive feedback to trainees. Improving the accuracy of video-based metrics computed using object detection is generally done by improving the performance of the object detection network, however increasing its performance requires resources that cannot always be obtained. This study aims to improve the accuracy of metrics in central venous catheterization without requiring a high performing object detection network by removing false positive predictions identified using uncertainty quantification. The uncertainty for each bounding box was calculated using an entropy equation. The uncertainties were then compared to an uncertainty threshold computed using the optimal point of a Receiver Operating Characteristic curve. Predictions were removed if the uncertainty fell below the predefined threshold. 50 videos were recorded and \u2026<\/div>
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