no code implementations • 8 Aug 2022 • Thomas Schlegl, Heiko Stino, Michael Niederleithner, Andreas Pollreisz, Ursula Schmidt-Erfurth, Wolfgang Drexler, Rainer A. Leitgeb, Tilman Schmoll
The automatic detection and localization of anatomical features in retinal imaging data are relevant for many aspects.
no code implementations • 29 May 2019 • Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl, Sebastian M. Waldstein, Hrvoje Bogunović, Sophie Klimscha, Georg Langs, Ursula Schmidt-Erfurth
We propose to take advantage of this property using bayesian deep learning, based on the assumption that epistemic uncertainties will correlate with anatomical deviations from a normal training set.
no code implementations • 31 Oct 2018 • Philipp Seeböck, Sebastian M. Waldstein, Sophie Klimscha, Hrvoje Bogunovic, Thomas Schlegl, Bianca S. Gerendas, René Donner, Ursula Schmidt-Erfurth, Georg Langs
A multi-scale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data.
no code implementations • 8 May 2018 • Thomas Schlegl, Hrvoje Bogunovic, Sophie Klimscha, Philipp Seeböck, Amir Sadeghipour, Bianca Gerendas, Sebastian M. Waldstein, Georg Langs, Ursula Schmidt-Erfurth
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring.
18 code implementations • 17 Mar 2017 • Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein, Ursula Schmidt-Erfurth, Georg Langs
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
Generative Adversarial Network Unsupervised Anomaly Detection
no code implementations • 2 Dec 2016 • Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha, Bianca S. Gerendas, René Donner, Thomas Schlegl, Ursula Schmidt-Erfurth, Georg Langs
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice.