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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)

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Robert Seeböck

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Correction to "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. IEEE Trans. Med. Imaging, 39 (4): 1291 (2020)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 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)Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT.D. Lachinov, P. Seeböck, J. Mai, U. Schmidt-Erfurth, and H. Bogunovic. CoRR, (2021)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 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)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)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)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)

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Disambiguation of "Seeböck, Philipp"

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

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Robert Seeböck

Philipp Werner

Philipp Runge

Philipp Romero

Philipp Klug

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Disambiguation of "Seeböck, Philipp"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML U2-Net: A Bayesian U-Net Model With Epistemic Uncertainty Feedback For Photoreceptor Layer Segmentation In Pathological OCT Scans.

Please choose a person to relate this publication to

Robert Seeböck

Philipp Werner

Philipp Runge

Philipp Romero

Philipp Klug

Other publications of authors with the same name

Disambiguation

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