引用次数
引用: 4,618
H-指数: 15
I10-指数 : 17
出版物: 39
Unsupervised deep learning to identify markers in optical coherence tomography
Georg Langs , Ursula Schmidt-Erfurth , Bianca S Gerendas , Sebastian M Waldstein
Investigative Ophthalmology & Visual Science 59 ( 9) 1736 -1736
2018
Ability of eye-care professionals in grading retinal fluid volumes and change in age-related macular degeneration assessed by automated fluid monitoring
Martin Michl , Bianca S Gerendas , Anastasiia Gruber , Philipp Seeboeck
Investigative Ophthalmology & Visual Science 64 ( 8) 2167 -2167
2023
Defining disease endophenotypes in neovascular AMD by unsupervised machine learning of large-scale OCT data
Georg Langs , Ursula Schmidt-Erfurth , Bianca S Gerendas , Sebastian M Waldstein
Investigative Ophthalmology & Visual Science 58 ( 8) 56 -56
2017
Detection of retinal fluids in OCT scans by an automated deep learning algorithm compared to human expert grading in the HAWK & HARRIER trials
Ursula Schmidt-Erfurth , Hrvoje Bogunovic , Bianca S S. Gerendas , John Seaman
Investigative Ophthalmology & Visual Science 61 ( 7) 5187 -5187
2020
Linking Function and Structure: Prediction of Retinal Sensitivity in AMD from OCT using Deep Learning
Ursula Schmidt-Erfurth , Magdalena Baratsits , Hrvoje Bogunovic , Georgios Mylonas
Investigative Ophthalmology & Visual Science 60 ( 9) 1534 -1534
2019
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.
Thomas Schlegl , Philipp Seeböck , Sebastian M. Waldstein , Georg Langs
Medical Image Analysis 54 30 -44
686
2019
Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy.
Martin Michl , Maria Fabianska , Philipp Seeböck , Amir Sadeghipour
British Journal of Ophthalmology
18
2020
Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.
Sebastian M. Waldstein , Philipp Seeböck , René Donner , Amir Sadeghipour
Scientific Reports 10 ( 1) 12954
3
2020
Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina.
David Romo-Bucheli , Philipp Seeböck , José Ignacio Orlando , Bianca S. Gerendas
Biomedical Optics Express 11 ( 1) 346 -363
6
2020
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Thomas Schlegl , Philipp Seeböck , Sebastian M. Waldstein , Ursula Schmidt-Erfurth
international conference information processing 146 -157
1,902
2017
AI-based monitoring of retinal fluid in disease activity and under therapy
Ursula Schmidt-Erfurth , Gregor S Reiter , Sophie Riedl , Philipp Seeböck
Progress in retinal and eye research 86 100972
23
2022
Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning
Simon Schürer-Waldheim , Philipp Seeböck , Hrvoje Bogunović , Bianca S Gerendas
IEEE Journal of Biomedical and Health Informatics 26 ( 8) 3927 -3937
2022
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Burak Kocak , Tugba Akinci D’Antonoli , Nathaniel Mercaldo , Angel Alberich-Bayarri
Insights into imaging 15 ( 1) 8 -8
14
2024
Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT
Philipp Seeböck , José Ignacio Orlando , Thomas Schlegl , Sebastian M Waldstein
IEEE transactions on medical imaging 39 ( 1) 87 -98
148
2019
Unsupervised identification of disease marker candidates in retinal OCT imaging data
Philipp Seeböck , Sebastian M Waldstein , Sophie Klimscha , Hrvoje Bogunovic
IEEE transactions on medical imaging 38 ( 4) 1037 -1047
98
2018
Fully automated segmentation of hyperreflective foci in optical coherence tomography images
Thomas Schlegl , Hrvoje Bogunovic , Sophie Klimscha , Philipp Seeböck
arXiv preprint arXiv:1805.03278
29
2018
Projective skip-connections for segmentation along a subset of dimensions in retinal OCT
Dmitrii Lachinov , Philipp Seeböck , Julia Mai , Felix Goldbach
Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part I 24 431 -441
19
2021
Linking function and structure with ReSensNet: predicting retinal sensitivity from OCT using deep learning
Philipp Seeböck , Wolf-Dieter Vogl , Sebastian M Waldstein , Jose Ignacio Orlando
Ophthalmology Retina 6 ( 6) 501 -511
10
2022
Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degeneration
Gregor S Reiter , Hrvoje Bogunovic , Ferdinand Schlanitz , Wolf-Dieter Vogl
Eye 37 ( 17) 3582 -3588
2
2023
The impact of drusen on retinal sensitivity in non-exudative age-related macular degeneration: a point-to-point analysis
Ferdinand Georg Schlanitz , Hrvoje Bogunovic , Wolf-Dieter Vogl , Philipp Seeböck
Investigative Ophthalmology & Visual Science 61 ( 7) 1822 -1822
1
2020