E
Ender Konukoglu
Researcher at ETH Zurich
Publications - 200
Citations - 12500
Ender Konukoglu is an academic researcher from ETH Zurich. The author has contributed to research in topics: Segmentation & Computer science. The author has an hindex of 43, co-authored 182 publications receiving 9747 citations. Previous affiliations of Ender Konukoglu include Beijing Institute of Technology & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Book
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold, Learning and Semi-supervised Learning
TL;DR: A unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision, and medical image analysis tasks is presented and relative advantages and disadvantages discussed.
Book ChapterDOI
Regression forests for efficient anatomy detection and localization in CT studies
TL;DR: This paper introduces a new, continuous parametrization of the anatomy localization task which is effectively addressed by regression forests, and shows to be a more natural approach than classification.
Journal ArticleDOI
Shape-based hand recognition
TL;DR: Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
TL;DR: A unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision and medical image analysis tasks and how alternatives such as random ferns and extremely randomized trees stem from the more general model is discussed.