T
Tammy Riklin Raviv
Researcher at Ben-Gurion University of the Negev
Publications - 37
Citations - 4264
Tammy Riklin Raviv is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 13, co-authored 34 publications receiving 3074 citations. Previous affiliations of Tammy Riklin Raviv include Massachusetts Institute of Technology.
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.
Proceedings ArticleDOI
Real-time abnormal motion detection in surveillance video
TL;DR: A novel solution for real-time abnormal motion detection that is well-suited for modern video-surveillance architectures, where limited computing power is available near the camera for compression and communication.
Journal ArticleDOI
From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.
TL;DR: It is shown that this method substantially increased the replicability of explanation maps, converged with results from voxel‐based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.
Journal ArticleDOI
A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation
Eva Dittrich,Eva Dittrich,Tammy Riklin Raviv,Gregor Kasprian,René Donner,Peter Brugger,Daniela Prayer,Georg Langs,Georg Langs +8 more
TL;DR: This work proposes a method for the semi-supervised learning of a spatio-temporal latent atlas of fetal brain development, and corresponding segmentations of emerging cerebral structures, such as the ventricles or cortex, based on the annotation of a few examples and a large number of imaging data without annotation.
Proceedings ArticleDOI
Microscopy Cell Segmentation Via Convolutional LSTM Networks
Assaf Arbelle,Tammy Riklin Raviv +1 more
TL;DR: In this article, the authors proposed a novel segmentation architecture which integrates Convolutional Long Short Term Memory (C-LSTM) with the U-Net to capture multi-scale, compact, spatio-temporal encoding in the memory units.