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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

T. Schlegl, P. Seeböck, S. Waldstein, G. Langs, and U. Schmidt-Erfurth. Medical Image Anal., (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)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)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)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 (1): 87-98 (2020)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)f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.T. Schlegl, P. Seeböck, S. Waldstein, G. Langs, and U. Schmidt-Erfurth. Medical Image Anal., (2019)Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation.P. Seeböck, D. Romo-Bucheli, S. Waldstein, H. Bogunovic, J. Orlando, B. Gerendas, G. Langs, and U. Schmidt-Erfurth. ISBI, page 605-609. IEEE, (2019)

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Disambiguation of "Seeböck, Philipp"

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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

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Robert Seeböck

Philipp Romero

Philipp Werner

Philipp Runge

Philipp Gerhold

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BibSonomy

Disambiguation of "Seeböck, Philipp"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Please choose a person to relate this publication to

Robert Seeböck

Philipp Romero

Philipp Werner

Philipp Runge

Philipp Gerhold

Other publications of authors with the same name

Disambiguation

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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.