Ender Konukoglu
ETH Zurich
217 Papers
990 Citations
Ender Konukoglu is an academic researcher from ETH Zurich. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 43, co-authored 182 publications. Previous affiliations of Ender Konukoglu include Beijing Institute of Technology & French Institute for Research in Computer Science and Automation.
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Papers
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
Antonio Criminisi,Jamie Shotton,Ender Konukoglu +2 more
- 14 Mar 2012
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.
•Posted Content
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
TL;DR: In this article, a pixel-wise contrastive framework is proposed to enforce pixel embeddings belonging to a same semantic class to be more similar than embedding from different classes.
412
Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR.
Darko Zikic,Ben Glocker,Ender Konukoglu,Antonio Criminisi,Çağatay Demiralp,Jamie Shotton,Owen M. Thomas,Tilak Das,Raj Jena,Stephen J. Price +9 more
- 01 Oct 2012
TL;DR: The discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest.
Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images.
Ezequiel Geremia,Olivier Clatz,Bjoern H. Menze,Ender Konukoglu,Antonio Criminisi,Nicholas Ayache +5 more
TL;DR: In an a posteriori analysis, it is shown how selected features during classification can be ranked according to their discriminative power and reveal the most important ones.
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