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Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy:...

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy:...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10615926

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

About this item

Full title

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

Publisher

New York: Springer US

Journal title

Surgical endoscopy, 2023-11, Vol.37 (11), p.8577-8593

Language

English

Formats

Publication information

Publisher

New York: Springer US

More information

Scope and Contents

Contents

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 lea...

Alternative Titles

Full title

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10615926

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10615926

Other Identifiers

ISSN

0930-2794

E-ISSN

1432-2218

DOI

10.1007/s00464-023-10447-6

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