F
Florence Forbes
Researcher at University of Grenoble
Publications - 180
Citations - 8334
Florence Forbes is an academic researcher from University of Grenoble. The author has contributed to research in topics: Expectation–maximization algorithm & Image segmentation. The author has an hindex of 26, co-authored 162 publications receiving 6809 citations. Previous affiliations of Florence Forbes include French Institute for Research in Computer Science and Automation.
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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.
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Deviance information criteria for missing data models
TL;DR: The deviance information criterion is reassessed for missing data models, testing the behaviour of variousextensions in the cases of mixture and random models.
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Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study
TL;DR: This study compares the relative performances of the Bayesian clustering computer programs STRUCTURE, GENELAND, GENECLUST and a new program named TESS to suggest that combining analyses using TESS and STRUCTURES offers a convenient way to address inference of spatial population structure.
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EM Procedures Using Mean Field-Like Approximations for Markov Model-Based Image Segmentation
TL;DR: A class of algorithms in which the idea is to deal with systems of independent variables corresponds to approximations of the pixels' interactions similar to the mean field approximation, and follows algorithms that have the advantage of taking the Markovian structure into account while preserving the good features of EM.
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Rigid and Articulated Point Registration with Expectation Conditional Maximization
TL;DR: An innovative EM-like algorithm, namely, the Expectation Conditional Maximization for Point Registration (ECMPR) algorithm, is introduced, which allows the use of general covariance matrices for the mixture model components and improves over the isotropic covariance case.