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Philipp Seeböck

Showing results (1-10 of 17) with videos related to

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Medical Image Analysis|March 5, 2019
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networksThomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, et al.
Acta Ophthalmologica|March 28, 2022
Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learningLuca Schwarzenbacher, Philipp Seeböck, Daniel Schartmüller, et al.
Medical Image Analysis|February 13, 2024
Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detectionPhilipp Seeböck, José Ignacio Orlando, Martin Michl, et al.
European Radiology Experimental|June 6, 2023
Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility studyBianca Burger, Maria Bernathova, Philipp Seeböck, et al.
Biomedical Optics Express|July 1, 2022
Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus picturesDominik Hofer, Ursula Schmidt-Erfurth, José Ignacio Orlando, et al.
Journal of Ophthalmology|May 7, 2026
The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A ReviewAnna M Wittmann, Philipp Seeböck, Katharina A Heger, et al.
Scientific Reports|August 2, 2020
Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learningSebastian M Waldstein, Philipp Seeböck, René Donner, et al.
Eye (London, England)|May 11, 2023
Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degenerationGregor S Reiter, Hrvoje Bogunovic, Ferdinand Schlanitz, et al.
The British Journal of Ophthalmology|November 23, 2022
Quality assessment of colour fundus and fluorescein angiography images using deep learningMichael König, Philipp Seeböck, Bianca S Gerendas, et al.
Eye (London, England)|July 1, 2022
Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learningDavid Holomcik, Philipp Seeböck, Bianca S Gerendas, et al.
Pageof 2

Showing results (1-10 of 17) with videos related to

Sort By:
Pageof 2
Medical Image Analysis|March 5, 2019
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networksThomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, et al.
Acta Ophthalmologica|March 28, 2022
Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learningLuca Schwarzenbacher, Philipp Seeböck, Daniel Schartmüller, et al.
Medical Image Analysis|February 13, 2024
Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detectionPhilipp Seeböck, José Ignacio Orlando, Martin Michl, et al.
European Radiology Experimental|June 6, 2023
Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility studyBianca Burger, Maria Bernathova, Philipp Seeböck, et al.
Biomedical Optics Express|July 1, 2022
Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus picturesDominik Hofer, Ursula Schmidt-Erfurth, José Ignacio Orlando, et al.
Journal of Ophthalmology|May 7, 2026
The Clinical Significance of Imaging Biomarkers Discoverable by Anomaly Detection Methods in Retinal Diseases: A ReviewAnna M Wittmann, Philipp Seeböck, Katharina A Heger, et al.
Scientific Reports|August 2, 2020
Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learningSebastian M Waldstein, Philipp Seeböck, René Donner, et al.
Eye (London, England)|May 11, 2023
Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degenerationGregor S Reiter, Hrvoje Bogunovic, Ferdinand Schlanitz, et al.
The British Journal of Ophthalmology|November 23, 2022
Quality assessment of colour fundus and fluorescein angiography images using deep learningMichael König, Philipp Seeböck, Bianca S Gerendas, et al.
Eye (London, England)|July 1, 2022
Segmentation of macular neovascularization and leakage in fluorescein angiography images in neovascular age-related macular degeneration using deep learningDavid Holomcik, Philipp Seeböck, Bianca S Gerendas, et al.
Pageof 2