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Surgical phase and instrument recognition: how to identify appropriate dataset splits

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Machine learning approaches can only be reliably evaluated if training, validation, and test data splits are representative and not affected by the absence of classes. Surgical workflow and instrument recognition are two tasks that are complicated in this manner, because of heavy data imbalances res...

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Main Authors: Kostiuchik, Georgii (Author) , Sharan, Lalith (Author) , Mayer, Benedikt (Author) , Wolf, Ivo (Author) , Preim, Bernhard (Author) , Engelhardt, Sandy (Author)
Format: Article (Journal)
Language:English
Published: 29 January 2024
In: International journal of computer assisted radiology and surgery
Year: 2024, Volume: 19, Issue: 4, Pages: 699-711
ISSN:1861-6429
DOI:10.1007/s11548-024-03063-9
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11548-024-03063-9
Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s11548-024-03063-9
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Author Notes:Georgii Kostiuchik, Lalith Sharan, Benedikt Mayer, Ivo Wolf, Bernhard Preim, Sandy Engelhardt
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Surgical phase and instrument recognition: how to identify appropriate dataset splits by Kostiuchik, Georgii (Author) , Sharan, Lalith (Author) , Mayer, Benedikt (Author) , Wolf, Ivo (Author) , Preim, Bernhard (Author) , Engelhardt, Sandy (Author) ,


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