Retinal Vein Occlusion

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning
We introduced a fully automated approach to segment the photorceptor layer, evaluate its thickness and track potential disruptions using an ensemble of deep neural networks.
José Ignacio Orlando, Bianca S Gerendas, Sophie Riedl, Christoph Grechenig, Anna Breger, Martin Ehler, Sebastian M Waldstein, Hrvoje Bogunović, Ursula Schmidt-Erfurth
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Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning
On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning Problems
Anna introduced a new mathematical approach for dimensionality reduction that we incorporated into loss functions to augment target information and improve performance.
Anna Breger, José Ignacio Orlando, Pavol Harar, Monika Dörfler, Sophie Klimscha, Christoph Grechenig, Bianca S. Gerendas, Ursula Schmidt-Erfurth, Martin Ehler
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Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT
We used epistemic uncertainty estimates to discover potential abnormalities in diseased OCT scans. The uncertainty maps are obtained by a Bayesian U-Net trained on healthy OCT scans with weak labels of the retinal layers.
Philipp Seeböck, José Ignacio Orlando, Thomas Schlegl, Sebastian M. Waldstein, Hrvoje Bogunović, Sophie Klimscha, Georg Langs, Ursula Schmidt-Erfurth
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