Thomas Schlegl
Thomas Schlegl
Bestätigte E-Mail-Adresse bei meduniwien.ac.at
TitelZitiert vonJahr
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
2382017
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
672018
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
632015
Unsupervised pre-training across image domains improves lung tissue classification
T Schlegl, J Ofner, G Langs
International MICCAI Workshop on Medical Computer Vision, 82-93, 2014
342014
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
302017
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
252018
The relationship between eye movement and vision develops before birth
V Schöpf, T Schlegl, A Jakab, G Kasprian, R Woitek, D Prayer, G Langs
Frontiers in human neuroscience 8, 775, 2014
152014
Identifying and Categorizing Anomalies in Retinal Imaging Data
P Seeböck, S Waldstein, S Klimscha, BS Gerendas, R Donner, T Schlegl, ...
arXiv preprint arXiv:1612.00686, 2016
122016
Unsupervised Identification of Clinically Relevant Clusters in Routine Imaging Data
J Hofmanninger, M Krenn, M Holzer, T Schlegl, H Prosch, G Langs
International Conference on Medical Image Computing and Computer-Assisted …, 2016
102016
Unsupervised identification of disease marker candidates in retinal OCT imaging data
P Seeböck, SM Waldstein, S Klimscha, H Bogunovic, T Schlegl, ...
IEEE transactions on medical imaging 38 (4), 1037-1047, 2018
62018
Analyzing and Predicting Visual Acuity Outcomes of Anti-VEGF Therapy by a Longitudinal Mixed Effects Model of Imaging and Clinical Data
WD Vogl, SM Waldstein, BS Gerendas, T Schlegl, G Langs, ...
Investigative Ophthalmology & Visual Science 58 (10), 4173-4181, 2017
62017
Automatic segmentation and classification of intraretinal cystoid fluid and subretinal fluid in 3D-OCT using convolutional neural networks
T Schlegl, AM Glodan, D Podkowinski, SM Waldstein, BS Gerendas, ...
Investigative Ophthalmology & Visual Science 56 (7), 5920-5920, 2015
62015
A visual information retrieval system for radiology reports and the medical literature
D Markonis, R Donner, M Holzer, T Schlegl, S Dungs, S Kriewel, G Langs, ...
International Conference on Multimedia Modeling, 390-393, 2014
62014
Computational image analysis for prognosis determination in DME
BS Gerendas, H Bogunovic, A Sadeghipour, T Schlegl, G Langs, ...
Vision research 139, 204-210, 2017
52017
Spatial Correspondence Between Intraretinal Fluid, Subretinal Fluid, and Pigment Epithelial Detachment in Neovascular Age-Related Macular Degeneration
S Klimscha, SM Waldstein, T Schlegl, H Bogunović, A Sadeghipour, ...
Investigative Ophthalmology & Visual Science 58 (10), 4039-4048, 2017
52017
Medical Computer Vision: Algorithms for Big Data
T Schlegl, J Ofner, G Langs
Springer, 2014
52014
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
T Schlegl, H Bogunovic, S Klimscha, P Seeböck, A Sadeghipour, ...
arXiv preprint arXiv:1805.03278, 2018
32018
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
22019
COMPUTERIZED DEVICE AND METHOD FOR PROCESSING IMAGE DATA
T Schlegl, W Vogl, G Langs, S Waldstein, B Gerendas, U Schmidt-erfurth
US Patent App. 15/554,414, 2018
22018
Machine learning to predict the individual progression of AMD from imaging biomarkers
U Schmidt-Erfurth, H Bogunovic, S Klimscha, X Hu, T Schlegl, ...
Investigative Ophthalmology & Visual Science 58 (8), 3398-3398, 2017
22017
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