Philipp Seeböck
Philipp Seeböck
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Zitiert von
Zitiert von
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
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
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
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
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
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
AI-based monitoring of retinal fluid in disease activity and under therapy
U Schmidt-Erfurth, GS Reiter, S Riedl, P Seeböck, WD Vogl, BA Blodi, ...
Progress in retinal and eye research 86, 100972, 2022
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
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
Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy
M Michl, M Fabianska, P Seeböck, A Sadeghipour, BH Najeeb, ...
British Journal of Ophthalmology 106 (1), 113-120, 2022
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
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), 12954, 2020
Deep Learning In Medical Image Analysis
P Seeböck
Technical University of Vienna, 2015
Projective skip-connections for segmentation along a subset of dimensions in retinal OCT
D Lachinov, P Seeböck, J Mai, F Goldbach, U Schmidt-Erfurth, ...
Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021
Linking function and structure with ReSensNet: predicting retinal sensitivity from OCT using deep learning
P Seeböck, WD Vogl, SM Waldstein, JI Orlando, M Baratsits, T Alten, ...
Ophthalmology Retina 6 (6), 501-511, 2022
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
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
B Kocak, T Akinci D’Antonoli, N Mercaldo, A Alberich-Bayarri, B Baessler, ...
Insights into imaging 15 (1), 8, 2024
Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learning
D Holomcik, P Seeböck, BS Gerendas, G Mylonas, BH Najeeb, ...
Eye 37 (7), 1439-1444, 2023
Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology
B Kocak, LL Chepelev, LC Chu, R Cuocolo, BS Kelly, P Seeböck, ...
European radiology 33 (11), 7556-7560, 2023
Quality assessment of colour fundus and fluorescein angiography images using deep learning
M König, P Seeböck, BS Gerendas, G Mylonas, R Winklhofer, ...
British Journal of Ophthalmology 108 (1), 98-104, 2024
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