| 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 | 3123 | 2017 |
| 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 | 1408 | 2019 |
| 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 | 564 | 2018 |
| 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 | 213 | 2018 |
| 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 | 169 | 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 |
| 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 | 107 | 2018 |
| Unsupervised pre-training across image domains improves lung tissue classification T Schlegl, J Ofner, G Langs Medical Computer Vision: Algorithms for Big Data: International Workshop …, 2014 | 90 | 2014 |
| 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 | 62 | 2016 |
| Computational image analysis for prognosis determination in DME BS Gerendas, H Bogunovic, A Sadeghipour, T Schlegl, G Langs, ... Vision research 139, 204-210, 2017 | 58 | 2017 |
| 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 | 40 | 2017 |
| 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 | 39 | 2017 |
| International conference on information processing in medical imaging T Schlegl, P Seeböck, SM Waldstein, U Schmidt-Erfurth, G Langs Springer,, 2017 | 39* | 2017 |
| Ultra-widefield OCT Angiography M Niederleithner, L De Sisternes, H Stino, A Sedova, T Schlegl, ... IEEE Transactions on Medical Imaging, 2022 | 36 | 2022 |
| 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 | 34 | 2014 |
| 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 | 31 | 2018 |
| Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography S Röhrich, T Schlegl, C Bardach, H Prosch, G Langs European radiology experimental 4 (1), 1-11, 2020 | 30 | 2020 |
| Unsupervised identification of clinically relevant clusters in routine imaging data J Hofmanninger, M Krenn, M Holzer, T Schlegl, H Prosch, G Langs Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 17 | 2016 |
| Detection of diabetic neovascularisation using single-capture 65°-widefield optical coherence tomography angiography H Stino, M Niederleithner, J Iby, A Sedova, T Schlegl, I Steiner, S Sacu, ... British Journal of Ophthalmology, 2022 | 15 | 2022 |
Georg LangsMedical University of Vienna, CIR LabVerified email at meduniwien.ac.at
