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

D. Hofer, J. Orlando, P. Seeböck, G. Mylonas, F. Goldbach, A. Sadeghipour, B. Gerendas, and U. Schmidt-Erfurth. OMIA@MICCAI, volume 11855 of Lecture Notes in Computer Science, page 35-42. Springer, (2019)

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Robert Seeböck

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Identifying and Categorizing Anomalies in Retinal Imaging Data.P. Seeböck, S. Waldstein, S. Klimscha, B. Gerendas, R. Donner, T. Schlegl, U. Schmidt-Erfurth, and G. Langs. CoRR, (2016)Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.P. Seeböck, S. Waldstein, S. Klimscha, H. Bogunovic, T. Schlegl, B. Gerendas, R. Donner, U. Schmidt-Erfurth, and G. Langs. IEEE Trans. Med. Imaging, 38 (4): 1037-1047 (2019)Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning.D. Hofer, J. Orlando, P. Seeböck, G. Mylonas, F. Goldbach, A. Sadeghipour, B. Gerendas, and U. Schmidt-Erfurth. OMIA@MICCAI, volume 11855 of Lecture Notes in Computer Science, page 35-42. Springer, (2019)Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery.T. Schlegl, P. Seeböck, S. Waldstein, U. Schmidt-Erfurth, and G. Langs. IPMI, volume 10265 of Lecture Notes in Computer Science, page 146-157. Springer, (2017)Detection of Emerging Infectious Diseases in Lung CT Based on Spatial Anomaly Patterns.B. Mitic, P. Seeböck, J. Straub, H. Prosch, and G. Langs. MLMI@MICCAI (2), volume 15242 of Lecture Notes in Computer Science, page 138-147. Springer, (2024)Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT.D. Lachinov, P. Seeböck, J. Mai, F. Goldbach, U. Schmidt-Erfurth, and H. Bogunovic. MICCAI (1), volume 12901 of Lecture Notes in Computer Science, page 431-441. Springer, (2021)Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning.S. Schürer-Waldheim, P. Seeböck, H. Bogunovic, B. Gerendas, and U. Schmidt-Erfurth. IEEE J. Biomed. Health Informatics, 26 (8): 3927-3937 (2022)U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans.J. Orlando, P. Seeböck, H. Bogunovic, S. Klimscha, C. Grechenig, S. Waldstein, B. Gerendas, and U. Schmidt-Erfurth. ISBI, page 1441-1445. IEEE, (2019)Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.P. Seeböck, S. Waldstein, S. Klimscha, H. Bogunovic, T. Schlegl, B. Gerendas, R. Donner, U. Schmidt-Erfurth, and G. Langs. CoRR, (2018)Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.P. Seeböck, J. Orlando, T. Schlegl, S. Waldstein, H. Bogunovic, S. Klimscha, G. Langs, and U. Schmidt-Erfurth. CoRR, (2019)

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Disambiguation of "Seeböck, Philipp"

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

Please choose a person to relate this publication to

Robert Seeböck

Philipp Runge

Philipp Romero

Philipp Werner

Philipp Lang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Seeböck, Philipp"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning.

Please choose a person to relate this publication to

Robert Seeböck

Philipp Runge

Philipp Romero

Philipp Werner

Philipp Lang

Other publications of authors with the same name

Disambiguation

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URL
DOI
BibTeX
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Foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning.