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Georg Langs
Georg Langs
Medical University of Vienna, CIR Lab
Bestätigte E-Mail-Adresse bei meduniwien.ac.at - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
29532017
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
17422016
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
14402019
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
13142019
Introduction to radiomics
ME Mayerhoefer, A Materka, G Langs, I Häggström, P Szczypiński, ...
Journal of Nuclear Medicine 61 (4), 488-495, 2020
11312020
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
5372018
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
4952015
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
4512020
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
4022020
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
2782018
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
2312006
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
2142011
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
2032018
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
2022016
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
1802017
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
1752020
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
1732020
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
1612019
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
1602015
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
1472014
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