Philipp Seeböck

发表

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Georg Langs, Thomas Schlegl, Philipp Seeböck, 2017, IPMI.

Unsupervised Learning for Image Category Detection

G. Langs, T. Schlegl, Philipp Seeböck, 2017 .

Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learning

Philipp Seeböck, B. Gerendas, B. Najeeb, 2022, Eye.

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning

U. Schmidt-Erfurth, Philipp Seeböck, B. Gerendas, 2022, IEEE Journal of Biomedical and Health Informatics.

Retinal OCT Analysis and Prediction with Deep Learning

Philipp Seeböck, H. Bogunović, Wolf-Dieter Vogl, 2020 .

AI-based monitoring of retinal fluid in disease activity and under therapy

U. Schmidt-Erfurth, A. Fawzi, Yali Jia, 2021, Progress in Retinal and Eye Research.

Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina.

José Ignacio Orlando, Philipp Seeböck, Ursula Schmidt-Erfurth, 2019, Biomedical optics express.

Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images pdf

Amir Sadeghipour, Georg Langs, Thomas Schlegl, 2018, ArXiv.

f‐AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Georg Langs, Thomas Schlegl, Ursula Schmidt-Erfurth, 2019, Medical Image Anal..

Identifying and Categorizing Anomalies in Retinal Imaging Data pdf

Georg Langs, Rene Donner, Thomas Schlegl, 2016, ArXiv.

Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy

U. Schmidt-Erfurth, Philipp Seeböck, B. Gerendas, 2020, British Journal of Ophthalmology.

Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning

U. Schmidt-Erfurth, Philipp Seeböck, R. Menapace, 2022, Acta ophthalmologica.

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

Georg Langs, José Ignacio Orlando, Thomas Schlegl, 2019, IEEE Transactions on Medical Imaging.

U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans

José Ignacio Orlando, Bianca S. Gerendas, Hrvoje Bogunovic, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learning

Philipp Seeböck, B. Gerendas, B. Najeeb, 2022, Eye.

Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning.

U. Schmidt-Erfurth, Philipp Seeböck, S. Waldstein, 2022, Ophthalmology. Retina.

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data

Georg Langs, Thomas Schlegl, Bianca S. Gerendas, 2018, IEEE Transactions on Medical Imaging.

Assessment of RadiomIcS rEsearch (ARISE): a brief guide for authors, reviewers, and readers from the Scientific Editorial Board of European Radiology

L. Chu, Philipp Seeböck, R. Cuocolo, 2023, European Radiology.

Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degeneration

Philipp Seeböck, H. Bogunović, Wolf-Dieter Vogl, 2023, Eye.

Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning

Amir Sadeghipour, Ursula Schmidt-Erfurth, Philipp Seeböck, 2020, Scientific Reports.

Quality assessment of colour fundus and fluorescein angiography images using deep learning

U. Schmidt-Erfurth, Philipp Seeböck, B. Gerendas, 2022, The British journal of ophthalmology.

Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation

Georg Langs, José Ignacio Orlando, Bianca S. Gerendas, 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning

Amir Sadeghipour, Georgios Mylonas, José Ignacio Orlando, 2019, OMIA@MICCAI.

Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection.

Philipp Seeböck, J. Orlando, H. Bogunović, 2024, Medical image analysis.

AI-based monitoring of retinal fluid in disease activity and under therapy

U. Schmidt-Erfurth, A. Fawzi, Yali Jia, 2021, Progress in Retinal and Eye Research.