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Situation Interpretation for Knowledge- and Model Based Laparoscopic Surgery

dc.contributor.authorKati?, Darko*
dc.date.accessioned2021-02-12T03:40:33Z
dc.date.available2021-02-12T03:40:33Z
dc.date.issued2016*
dc.date.submitted2019-07-30 20:01:58*
dc.identifier34632*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/59441
dc.description.abstractTo manage the influx of information into surgical practice, new man-machine interaction methods are necessary to prevent information overflow. This work presents an approach to automatically segment surgeries into phases and select the most appropriate pieces of information for the current situation. This way, assistance systems can adopt themselves to the needs of the surgeon and not the other way around.*
dc.languageEnglish*
dc.subjectQA75.5-76.95*
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer scienceen_US
dc.subject.otherMaschinelles Lernen*
dc.subject.otherAssistenz*
dc.subject.otherOntologie*
dc.subject.otherOntology*
dc.subject.otherSurgery*
dc.subject.otherAugmented Reality*
dc.subject.otherChirurgie*
dc.subject.otherErweiterte RealitätMachine Learning*
dc.subject.otherAssistance*
dc.titleSituation Interpretation for Knowledge- and Model Based Laparoscopic Surgery*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000054609*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783731505273*
oapen.pagesXV, 148 p.*
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityScientific or Editorial Board
peerreview.id8ad5c235-9810-49eb-b358-27c8675324d9
peerreview.titleDissertations (Dissertationen)


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