Zobrazeno 1 - 10
of 203
pro vyhledávání: '"Schlegl, Thomas"'
Autor:
Schlegl, Thomas, Stino, Heiko, Niederleithner, Michael, Pollreisz, Andreas, Schmidt-Erfurth, Ursula, Drexler, Wolfgang, Leitgeb, Rainer A., Schmoll, Tilman
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects. In this work, we follow a data-centric approach to optimize classifier training for optic nerve head detection and localization in
Externí odkaz:
http://arxiv.org/abs/2208.03868
Autor:
Kreminger, Judith *, Iby, Johannes, Rokitansky, Stephanie *, Stino, Heiko, Niederleithner, Michael, Schlegl, Thomas, Drexler, Wolfgang, Schmoll, Tilman, Leitgeb, Rainer, Pollreisz, Andreas, Schmidt-Erfurth, Ursula, Sacu, Stefan *
Publikováno v:
In Canadian Journal of Ophthalmology/Journal canadien d'ophtalmologie August 2024
Autor:
Stino, Heiko, Huber, Kim Lien, Niederleithner, Michael, Mahnert, Nikolaus, Sedova, Aleksandra, Schlegl, Thomas, Steiner, Irene, Sacu, Stefan, Drexler, Wolfgang, Schmoll, Tilman, Leitgeb, Rainer, Schmidt-Erfurth, Ursula, Pollreisz, Andreas
Publikováno v:
In Ophthalmology Retina December 2023 7(12):1042-1050
Autor:
Seeböck, Philipp, Orlando, José Ignacio, Schlegl, Thomas, Waldstein, Sebastian M., Bogunović, Hrvoje, Klimscha, Sophie, Langs, Georg, Schmidt-Erfurth, Ursula
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a-priori definitions of these regions
Externí odkaz:
http://arxiv.org/abs/1905.12806
Publikováno v:
In Procedia CIRP 2023 120:1185-1190
Autor:
Seeböck, Philipp, Waldstein, Sebastian M., Klimscha, Sophie, Bogunovic, Hrvoje, Schlegl, Thomas, Gerendas, Bianca S., Donner, René, Schmidt-Erfurth, Ursula, Langs, Georg
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation o
Externí odkaz:
http://arxiv.org/abs/1810.13404
Autor:
Schlegl, Thomas, Bogunovic, Hrvoje, Klimscha, Sophie, Seeböck, Philipp, Sadeghipour, Amir, Gerendas, Bianca, Waldstein, Sebastian M., Langs, Georg, Schmidt-Erfurth, Ursula
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amounts of data and the corresponding study of disease characteristics. The
Externí odkaz:
http://arxiv.org/abs/1805.03278
Publikováno v:
In Advanced Engineering Informatics April 2022 52
Autor:
Schlegl, Thomas, Seeböck, Philipp, Waldstein, Sebastian M., Schmidt-Erfurth, Ursula, Langs, Georg
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High
Externí odkaz:
http://arxiv.org/abs/1703.05921
Autor:
Seeböck, Philipp, Waldstein, Sebastian, Klimscha, Sophie, Gerendas, Bianca S., Donner, René, Schlegl, Thomas, Schmidt-Erfurth, Ursula, Langs, Georg
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a prio
Externí odkaz:
http://arxiv.org/abs/1612.00686