Active learning using deep Bayesian networks for surgical workflow analysis
Purpose For many applications in the field of computer-assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
9 April 2019
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| In: |
International journal of computer assisted radiology and surgery
Year: 2019, Volume: 14, Issue: 6, Pages: 1079-1087 |
| ISSN: | 1861-6429 |
| DOI: | 10.1007/s11548-019-01963-9 |
| Online Access: | Verlag, Volltext: https://doi.org/10.1007/s11548-019-01963-9 Verlag, Volltext: https://link.springer.com/article/10.1007/s11548-019-01963-9 
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| Author Notes: | Sebastian Bodenstedt, Dominik Rivoir, Alexander Jenke, Martin Wagner, Michael Breucha, Beat Müller-Stich, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel |
| Summary: | Purpose For many applications in the field of computer-assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the... |
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| Item Description: | Gesehen am 11.09.2019 |
| Physical Description: | Online Resource |
| ISSN: | 1861-6429 |
| DOI: | 10.1007/s11548-019-01963-9 |