172 citations · 484 across the 6 of their papers we have counts for
6 papers
Data-centric AI approach to improve optic nerve head segmentation and localization in OCT en face images
Thomas Schlegl, Heiko Stino, Michael Niederleithner +5
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 c…
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl +5
Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological a…
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Philipp Seeböck, Sebastian M. Waldstein, Sophie Klimscha +6
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection a…
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
Thomas Schlegl, Hrvoje Bogunovic, Sophie Klimscha +6
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 amoun…
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein +2
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 ann…
Identifying and Categorizing Anomalies in Retinal Imaging Data
Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha +5
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly super…