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Expert driven semi-supervised elucidation tool for medical endoscopic videos

Albisser, Zeno, Riegler, Michael, Halvorsen, Pål, Zhou, Jiang ORCID: 0000-0002-3067-8512, Griwodz, Carsten, Balasingham, IIangko and Gurrin, Cathal ORCID: 0000-0003-2903-3968 (2015) Expert driven semi-supervised elucidation tool for medical endoscopic videos. In: MMSys 2015, 18-20 March 2015, Portland, Oregon. ISBN 978-1-4503-3351-1

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Abstract

In this paper, we present a novel application for elucidating all kind of videos that require expert knowledge, e.g., sport videos, medical videos etc., focusing on endoscopic surgery and video capsule endoscopy. In the medical domain, the knowledge of experts for tagging and interpretation of videos is of high value. As a result of the stressful working environment of medical doctors, they often simply do not have time for extensive annotations. We therefore present a semi-supervised method to gather the annotations in a very easy and time saving way for the experts and we show how this information can be used later on.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Endoscopic video; Polyp; Semi-supervised
Subjects:Computer Science > Image processing
Computer Science > Information retrieval
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: MMSys '15 Proceedings of the 6th ACM Multimedia Systems Conference. . Association for Computing Machinery. ISBN 978-1-4503-3351-1
Publisher:Association for Computing Machinery
Official URL:http://dx.doi.org/10.1145/2713168.2713184
Copyright Information:© ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in MMSys '15 Proceedings of the 6th ACM Multimedia Systems Conference http://doi.acm.org/10.1145/2713168.2713184
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:20509
Deposited On:25 Mar 2015 11:52 by Jiang Zhou . Last Modified 25 Jan 2019 10:14

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