Philipp Seeböck
Title
Cited by
Cited by
Year
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
6772017
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
982019
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 38 (4), 1037-1047, 2018
262018
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
232016
A safe CFT at large charge
D Orlando, S Reffert, F Sannino
Journal of High Energy Physics 2019 (8), 164, 2019
182019
U2-net: A bayesian u-net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological oct scans
JI Orlando, P Seeböck, H Bogunović, S Klimscha, C Grechenig, ...
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019
162019
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
P Seeböck, JI Orlando, T Schlegl, SM Waldstein, H Bogunovic, ...
IEEE Transactions on Medical Imaging 39 (1), 87 - 98, 2019
132019
Using cyclegans for effectively reducing image variability across oct devices and improving retinal fluid segmentation
P Seeböck, D Romo-Bucheli, S Waldstein, H Bogunovic, JI Orlando, ...
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019
92019
International Conference on Information Processing in Medical Imaging
T Schlegl, P Seeböck, SM Waldstein, U Schmidt-Erfurth, G Langs
92017
Deep Learning In Medical Image Analysis
P Seeböck
Technical University of Vienna, 2015
92015
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
82018
Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina
D Romo-Bucheli, P Seeböck, JI Orlando, BS Gerendas, SM Waldstein, ...
Biomedical Optics Express 11 (1), 346-363, 2020
52020
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery (2017)
T Schlegl, P Seeböck, SM Waldstein, U Schmidt-Erfurth, G Langs
arXiv preprint arXiv:1703.05921, 0
3
Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning
SM Waldstein, P Seeböck, R Donner, A Sadeghipour, H Bogunović, ...
Scientific reports 10 (1), 1-9, 2020
22020
Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning
D Hofer, JI Orlando, P Seeböck, G Mylonas, F Goldbach, A Sadeghipour, ...
International Workshop on Ophthalmic Medical Image Analysis, 35-42, 2019
12019
Detection of retinal fluids in OCT scans by an automated deep learning algorithm compared to human expert grading in the HAWK & HARRIER trials
H Bogunovic, J Seaman, P Margaron, P Seeböck, BSS Gerendas, ...
Investigative Ophthalmology & Visual Science 61 (7), 5187-5187, 2020
2020
The Impact of Drusen on Retinal Sensitivity in non-exudative Age-Related Macular Degeneration: A point-to-point Analysis
FG Schlanitz, H Bogunovic, WD Vogl, P Seeböck, M Baratsits, S Sacu, ...
Investigative Ophthalmology & Visual Science 61 (7), 1822-1822, 2020
2020
Retinal OCT Analysis and Prediction with Deep Learning
H Bogunović, WD Vogl, P Seeböck, A Rivail
Optical Coherence Tomography, OW2E. 1, 2020
2020
Correction to “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 (4), 1291-1291, 2020
2020
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), 2019
2019
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