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Alexandre Kirszenberg
Researcher at École Pour l'Informatique et les Techniques Avancées
Publications - 7
Citations - 154
Alexandre Kirszenberg is an academic researcher from École Pour l'Informatique et les Techniques Avancées. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 2, co-authored 5 publications receiving 44 citations.
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Journal ArticleDOI
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina,Malek El Husseini,Amirhossein Bayat,Maximilian T. Löffler,Hans Liebl,Hongwei Li,Giles Tetteh,Jan Kukačka,Christian Payer,Darko Štern,Martin Urschler,Maodong Chen,Dalong Cheng,Nikolas Lessmann,Yujin Hu,Tianfu Wang,Dong Yang,Daguang Xu,Felix Ambellan,Tamaz Amiranashvili,Moritz Ehlke,Hans Lamecker,Sebastian Lehnert,Marilia Lirio,Nicolás Pérez de Olaguer,Heiko Ramm,Manish Sahu,Alexander Tack,Stefan Zachow,Tao Jiang,Xinjun Ma,Christoph Angerman,Xin Wang,Kevin W. Brown,Alexandre Kirszenberg,Elodie Puybareau,Di Chen,Yiwei Bai,Brandon H. Rapazzo,Timyoas Yeah,Amber Zhang,Shangliang Xu,Feng Hou,Zhiqiang He,Chan Zeng,Zheng Xiangshang,Xu Liming,Tucker Netherton,Raymond P. Mumme,Laurence E. Court,Zixun Huang,Chenhang He,Li-Wen Wang,Sai Ho Ling,Lê Duy Huỳnh,Nicolas Boutry,Roman Jakubicek,Jiri Chmelik,Supriti Mulay,Mohanasankar Sivaprakasam,Johannes C. Paetzold,Suprosanna Shit,Ivan Ezhov,Benedikt Wiestler,Ben Glocker,Alexander Valentinitsch,Markus Rempfler,Björn H. Menze,Jan S. Kirschke +68 more
TL;DR: The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations.
Posted Content
VerSe: A Vertebrae Labelling and Segmentation Benchmark
Anjany Sekuboyina,Amirhossein Bayat,Malek El Husseini,Maximilian T. Löffler,Markus Rempfler,Jan Kukačka,Giles Tetteh,Alexander Valentinitsch,Christian Payer,Martin Urschler,Maodong Chen,Dalong Cheng,Nikolas Lessmann,Yujin Hu,Tianfu Wang,Dong Yang,Daguang Xu,Felix Ambellan,Stefan Zachowk,Tao Jiang,Xinjun Ma,Christoph Angerman,Xin Wang,Qingyue Wei,Kevin M. Brown,Matthias Wolf,Alexandre Kirszenberg,Élodie Puybareauq,Björn H. Menze,Jan S. Kirschke +29 more
TL;DR: A detailed performance analysis of eleven fully automated algorithms of the participating teams were submitted to be benchmarked on the VerSe data and the best performing algorithm achieving a vertebrae identification rate of 95% and a Dice coefficient of 90%.
Book ChapterDOI
Going Beyond p-convolutions to Learn Grayscale Morphological Operators.
TL;DR: In this paper, two morphological layers based on the same principle as the p-convolutional layer are presented, which circumvent the principal drawbacks of p-CNN and demonstrate their potential interest in further implementations within deep convolutional neural network architectures.
Journal ArticleDOI
Learning Grayscale Mathematical Morphology with Smooth Morphological Layers
TL;DR: In this paper , two morphological layers based on the same principle as the p-convolution, while circumventing its principal drawbacks, were proposed, and showcased their capacity to efficiently learn grayscale morphological operators while raising several edge cases.
Book ChapterDOI
Go2Pins: A Framework for the LTL Verification of Go Programs
TL;DR: Go2Pins as discussed by the authors is a tool that takes a program written in Go and links it with two model-checkers: LTSMin [19] and Spot [7].