2023
DOI: 10.1007/s00464-023-10447-6
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Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

Johanna M. Brandenburg,
Alexander C. Jenke,
Antonia Stern
et al.

Abstract: Background With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features. Methods To… Show more

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Cited by 4 publications
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