Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Bora Baydar"'
Autor:
A. Emre Kavur, Naciye Sinem Gezer, Mustafa Barış, Yusuf Şahin, Savaş Özkan, Bora Baydar, Ulaş Yüksel, Çağlar Kılıkçıer, Şahin Olut, Gözde Bozdağı Akar, Gözde Ünal, Oğuz Dicle, M. Alper Selver
Publikováno v:
Diagnostic and Interventional Radiology, Vol 26, Iss 1, Pp 11-21 (2020)
PURPOSE:To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transp
Externí odkaz:
https://doaj.org/article/20139c9b3c984837993694b11ba5322f
Autor:
Savas Ozkan, N. Sinem Gezer, Dmitrii Lachinov, Debdoot Sheet, Fabian Isensee, Gozde Bozdagi Akar, M. Alper Selver, Soumick Chatterjee, Oliver Speck, A. Emre Kavur, Sinem Aslan, Josef Pauli, Oğuz Dicle, Gozde Unal, Pierre-Henri Conze, Andreas Nürnberger, Klaus H. Maier-Hein, Gurbandurdy Dovletov, Ronnie Rajan, Vladimir Groza, Rachana Sathish, Bora Baydar, Matthias Perkonigg, Shuo Han, Philipp Ernst, Duc Duy Pham, Mustafa Baris
Publikováno v:
Medical Image Analysis
Medical Image Analysis, Elsevier, 2021, 69, ⟨10.1016/j.media.2020.101950⟩
Medical Image Analysis, Elsevier, 2021, 69, ⟨10.1016/j.media.2020.101950⟩
Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::062a182f507db13c73a8422353093c1a
https://hdl.handle.net/10278/3736173
https://hdl.handle.net/10278/3736173
Autor:
Savas Ozkan, Mustafa Baris, Gozde Bozdagi Akar, Çağlar Kılıkçıer, Ulaş Yüksel, M. Alper Selver, Naciye Sinem Gezer, Sahin Olut, Bora Baydar, Oğuz Dicle, A. Emre Kavur, Gozde Unal, Yusuf Huseyin Sahin
Purpose To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b11052d7b6da45edef43704acf6410dc
https://aperta.ulakbim.gov.tr/record/6871
https://aperta.ulakbim.gov.tr/record/6871
Publikováno v:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI.
In this paper, a decision level fusion using multiple pre-screener algorithms is proposed for the detection of buried landmines from Ground Penetrating Radar (GPR) data. The Kernel Least Mean Square (KLMS) and the Blob Filter pre-screeners are fused
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ae34860ae0380b898d0c46ca91715f53
https://doi.org/10.1117/12.2223743
https://doi.org/10.1117/12.2223743
Autor:
Bora Baydar, Gozde Bozdagi Akar
Publikováno v:
SIU
This paper describes a novel pre-screener algorithm for landmine detection with a ground penetrating radar (GPR). The pre-screener algorithms are used for finding anomalies that are potential locations of interest. Thus, their processing time is as i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::175fd27fc208daef1051340394f7ea67
https://doi.org/10.1109/siu.2016.7495968
https://doi.org/10.1109/siu.2016.7495968