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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)

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Ursula Schmidt-Erfurth

Ivonne Erfurth

Jens Erfurth

Henning Erfurth

Fritz Erfurth

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Stable Registration of Pathological 3D SD-OCT Scans using Retinal VesselsJ. Wu, B. gerendas, S. Aldstein, G. Langs, C. simader, and U. Schmidt-Erfurth. Proceedings of MICCAI Ophtalmic Medical Image Analysis Workchop'14, (2014)Automated retinal fovea type distinction in spectral-domain optical coherence tomography of retinal vein occlusion.J. Wu, S. Waldstein, B. Gerendas, G. Langs, C. Simader, and U. Schmidt-Erfurth. Medical Imaging: Image Processing, volume 9413 of SPIE Proceedings, page 94133D. SPIE, (2015)Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease.J. Wu, S. Waldstein, A. Montuoro, B. Gerendas, G. Langs, and U. Schmidt-Erfurth. Int. J. Biomedical Imaging, (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)Metadata-enhanced contrastive learning from retinal optical coherence tomography images.R. Holland, O. Leingang, H. Bogunovic, S. Riedl, L. Fritsche, T. Prevost, H. Scholl, U. Schmidt-Erfurth, S. Sivaprasad, A. Lotery and 2 other author(s). CoRR, (2022)Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration.R. Holland, O. Leingang, C. Holmes, P. Anders, J. Paetzold, R. Kaye, S. Riedl, H. Bogunovic, U. Schmidt-Erfurth, L. Fritsche and 5 other author(s). CoRR, (2023)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)Specialist vision-language models for clinical ophthalmology.R. Holland, T. Taylor, C. Holmes, S. Riedl, J. Mai, M. Patsiamanidi, D. Mitsopoulou, P. Hager, P. Müller, H. Scholl and 6 other author(s). CoRR, (2024)Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning.A. Rivail, U. Schmidt-Erfurth, W. Vogl, S. Waldstein, S. Riedl, C. Grechenig, Z. Wu, and H. Bogunovic. PRIME@MICCAI, volume 11843 of Lecture Notes in Computer Science, page 44-52. Springer, (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 "Schmidt-Erfurth, Ursula"

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Identifying and Categorizing Anomalies in Retinal Imaging Data.

Please choose a person to relate this publication to

Ursula Schmidt-Erfurth

Ivonne Erfurth

Jens Erfurth

Henning Erfurth

Fritz Erfurth

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Schmidt-Erfurth, Ursula"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Identifying and Categorizing Anomalies in Retinal Imaging Data.

Please choose a person to relate this publication to

Ursula Schmidt-Erfurth

Ivonne Erfurth

Jens Erfurth

Henning Erfurth

Fritz Erfurth

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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Identifying and Categorizing Anomalies in Retinal Imaging Data.