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. Attached Files Name Description MIMEType Size Downloads Title Surgical tool segmentation using a hybrid deep CNN-RNN auto encoder-decoder Author(s) Attia, Mohammed Hassan Hossny, Mohammed orcid.org/0000-0002-1593-6296 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 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 Collections: Centre for Intelligent Systems Research GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 22 Jun 2018, 09:19:13 EST Sat, 23 Jun 2018, 05:00:19 EST Mon, 25 Jun 2018, 08:17:04 EST Tue, 20 Nov 2018, 05:01:49 EST Tue, 20 Nov 2018, 09:56:41 EST Tue, 20 Nov 2018, 09:59:07 EST Tue, 20 Nov 2018, 09:59:50 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 121 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Fri, 22 Jun 2018, 09:19:13 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.