Philipp Seeböck
Philipp Seeböck
Bestätigte E-Mail-Adresse bei meduniwien.ac.at - Startseite
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
2482017
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
132016
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
P Seeböck, S Waldstein, S Klimscha, H Bogunovic, T Schlegl, ...
IEEE Transactions on Medical Imaging, 2018
82018
Deep Learning In Medical Image Analysis
P Seeböck
Technical University of Vienna, 2015
62015
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
32019
A safe CFT at large charge
D Orlando, S Reffert, F Sannino
arXiv preprint arXiv:1905.00026, 2019
32019
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
Linking Function and Structure: Prediction of Retinal Sensitivity in AMD from OCT using Deep Learning
P Seeböck, WD Vogl, SM Waldstein, M Baratsits, JI Orlando, T Alten, ...
Investigative Ophthalmology & Visual Science 60 (9), 1534-1534, 2019
2019
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
P Seeböck, D Romo-Bucheli, S Waldstein, H Bogunović, JI Orlando, ...
arXiv preprint arXiv:1901.08379, 2019
2019
Unsupervised deep learning to identify markers in optical coherence tomography
SM Waldstein, P Seeboeck, R Donner, A Sadeghipour, G Langs, ...
Investigative Ophthalmology & Visual Science 59 (9), 1736-1736, 2018
2018
Defining disease endophenotypes in neovascular AMD by unsupervised machine learning of large-scale OCT data
P Seeböck, SM Waldstein, R Donner, BS Gerendas, A Sadeghipour, ...
Investigative Ophthalmology & Visual Science 58 (8), 56-56, 2017
2017
Unsupervised Learning for Image Category Detection
P Seeböck, R Donner, T Schlegl, G Langs
Proceedings of the 22nd Computer Vision Winter Workshop, 2017
2017
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