35 citations · 37 across the 2 of their papers we have counts for
5 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…
SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors
Botond Fazekas, Guilherme Aresta, Dmitrii Lachinov +4
Optical coherence tomography (OCT) is a non-invasive 3D modality widely used in ophthalmology for imaging the retina. Achieving automated, anatomically coherent retinal layer segme…
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
Taha Emre, Arunava Chakravarty, Antoine Rivail +3
Recent contrastive learning methods achieved state-of-the-art in low label regimes. However, the training requires large batch sizes and heavy augmentations to create multiple view…
Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm
Adnan Rashid, Sebastian M. Waldstein, Bianca S. Gerendas +8
PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DE…
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…