Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder

Attia, Mohammed Hassan, Hossny, Mohammed, Nahavandi, Saeid and Asadi, Hamed 2017, Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder, in IEEE SMC 2017 : Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, Institute of Electrical and Electronics Engineers, Piscataway, N.J., pp. 3373-3378, doi: 10.1109/SMC.2017.8123151.

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Title Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder
Author(s) Attia, Mohammed Hassan
Hossny, MohammedORCID iD for Hossny, Mohammed orcid.org/0000-0002-1593-6296
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Asadi, HamedORCID iD for Asadi, Hamed orcid.org/0000-0003-2475-9727
Conference name IEEE Systems, Man, and Cybernetics Society. Conference (2017 : Banff, Alta.)
Conference location Banff, Alta.
Conference dates 2017/10/05 - 2017/10/08
Title of proceedings IEEE SMC 2017 : Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics
Editor(s) [Unknown]
Publication date 2017
Series IEEE Systems, Man, and Cybernetics Society Conference
Start page 3373
End page 3378
Total pages 6
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Keyword(s) tools
feature extraction
image segmentation
surgery
semantics
recurrent neural networks
science & technology
technology
artificial intelligence
cybernetics
computer science
ISBN 9781538616451
Language eng
DOI 10.1109/SMC.2017.8123151
Field of Research 080106 Image Processing
Socio Economic Objective 920118 Surgical Methods and Procedures
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2017, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30109628

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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