Georg Langs

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Sebastian M. WaldsteinOphthalmology, Landesklinikum Mistelbach-GänserndorfAdresse e-mail validée de meduniwien.ac.at
Gregor KasprianMedical University of ViennaAdresse e-mail validée de meduniwien.ac.at
Thomas SchleglMedical University of ViennaAdresse e-mail validée de meduniwien.ac.at
René Donnermva.ai // contextflowAdresse e-mail validée de mva.ai
Ernst SchwartzMedical University ViennaAdresse e-mail validée de meduniwien.ac.at
Horst BischofGraz University of TechnologyAdresse e-mail validée de icg.tugraz.at
Karl-Heinz NenningNathan Kline Institute, Medizinische Universität WienAdresse e-mail validée de nki.rfmh.org
Henning MüllerProfessor in computer science at HES-SO & in radiology at the University of GenevaAdresse e-mail validée de hevs.ch
Bjoern MenzeUniversität ZürichAdresse e-mail validée de uzh.ch
Philipp SeeböckMedical University of ViennaAdresse e-mail validée de meduniwien.ac.at
Johannes HofmanningerResearch Associate, Medical University of ViennaAdresse e-mail validée de meduniwien.ac.at
Daniela PrayerProf of Radiology, Medical University ViennaAdresse e-mail validée de meduniwien.ac.at
Helmut ProschMedical University of ViennaAdresse e-mail validée de meduniwien.ac.at
Franz KainbergerMedical University of Vienna, RadiologyAdresse e-mail validée de meduniwien.ac.at
Allan HanburyTU Wien, Complexity Science Hub ViennaAdresse e-mail validée de tuwien.ac.at
Marc-André WeberProfessor der Radiologie, Universitätsmedizin RostockAdresse e-mail validée de med.uni-rostock.de
Markus HolzerResearch AssociateAdresse e-mail validée de meduniwien.ac.at
Daniel S. MarguliesCentre national de la recherche scientifique (CNRS)Adresse e-mail validée de cnrs.fr
András JakabResearch group leader, Faculty of Medicine, University of ZürichAdresse e-mail validée de kispi.uzh.ch
Hrvoje BogunovićMedical University of Vienna, AustriaAdresse e-mail validée de meduniwien.ac.at

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Georg Langs

Medical University of Vienna, CIR Lab

Adresse e-mail validée de meduniwien.ac.at - Page d'accueil

Machine Learning in NeuroImaging Functional Connectivity


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery T Schlegl, P Seeböck, SM Waldstein, U Schmidt-Erfurth, G Langs International conference on information processing in medical imaging, 146-157, 2017	3166	2017
Situating the default-mode network along a principal gradient of macroscale cortical organization DS Margulies, SS Ghosh, A Goulas, M Falkiewicz, JM Huntenburg, ... Proceedings of the National Academy of Sciences 113 (44), 12574-12579, 2016	1866	2016
Causability and explainability of artificial intelligence in medicine A Holzinger, G Langs, H Denk, K Zatloukal, H Müller Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9 (4 …, 2019	1601	2019
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks T Schlegl, P Seeböck, SM Waldstein, G Langs, U Schmidt-Erfurth Medical image analysis 54, 30-44, 2019	1425	2019
Introduction to radiomics ME Mayerhoefer, A Materka, G Langs, I Häggström, P Szczypiński, ... Journal of Nuclear Medicine 61 (4), 488-495, 2020	1317	2020
Fully automated detection and quantification of macular fluid in OCT using deep learning T Schlegl, SM Waldstein, H Bogunovic, F Endstraßer, A Sadeghipour, ... Ophthalmology 125 (4), 549-558, 2018	567	2018
Parcellating cortical functional networks in individuals D Wang, RL Buckner, MD Fox, DJ Holt, AJ Holmes, S Stoecklein, G Langs, ... Nature neuroscience 18 (12), 1853-1860, 2015	512	2015
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem J Hofmanninger, F Prayer, J Pan, S Röhrich, H Prosch, G Langs European Radiology Experimental 4 (1), 1-13, 2020	502	2020
BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets R Vos de Wael, O Benkarim, C Paquola, S Lariviere, J Royer, S Tavakol, ... Communications biology 3 (1), 103, 2020	436	2020
The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space J Klughammer, B Kiesel, T Roetzer, N Fortelny, A Nemc, KH Nenning, ... Nature medicine 24 (10), 1611-1624, 2018	291	2018
Fast active appearance model search using canonical correlation analysis R Donner, M Reiter, G Langs, P Peloschek, H Bischof Pattern Analysis and Machine Intelligence, IEEE Transactions on 28 (10 …, 2006	232	2006
The prenatal origin of hemispheric asymmetry: an in utero neuroimaging study G Kasprian, G Langs, PC Brugger, M Bittner, M Weber, M Arantes, ... Cerebral cortex 21 (5), 1076-1083, 2011	218	2011
Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration U Schmidt-Erfurth, H Bogunovic, A Sadeghipour, T Schlegl, G Langs, ... Ophthalmology Retina 2 (1), 24-30, 2018	214	2018
Continuous learning AI in radiology: implementation principles and early applications OS Pianykh, G Langs, M Dewey, DR Enzmann, CJ Herold, ... Radiology 297 (1), 6-14, 2020	212	2020
Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks O Jimenez-del-Toro, H Müller, M Krenn, K Gruenberg, AA Taha, ... IEEE transactions on medical imaging 35 (11), 2459-2475, 2016	208	2016
Cross-species functional alignment reveals evolutionary hierarchy within the connectome T Xu, KH Nenning, E Schwartz, SJ Hong, JT Vogelstein, A Goulas, ... NeuroImage, 117346, 2020	191	2020
Prediction of anti-VEGF treatment requirements in neovascular AMD using a machine learning approach H Bogunović, SM Waldstein, T Schlegl, G Langs, A Sadeghipour, X Liu, ... Investigative ophthalmology & visual science 58 (7), 3240-3248, 2017	188	2017
Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT P Seeböck, JI Orlando, T Schlegl, SM Waldstein, H Bogunović, ... IEEE transactions on medical imaging 39 (1), 87-98, 2019	173	2019
Predicting semantic descriptions from medical images with convolutional neural networks T Schlegl, SM Waldstein, WD Vogl, U Schmidt-Erfurth, G Langs International Conference on Information Processing in Medical Imaging, 437-448, 2015	166	2015
Fetal functional imaging portrays heterogeneous development of emerging human brain networks A Jakab, E Schwartz, G Kasprian, GM Gruber, D Prayer, V Schöpf, ... Frontiers in human neuroscience 8, 852, 2014	152	2014

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