172 citations · 396 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
José Ignacio Orlando, Philipp Seeböck, Hrvoje Bogunović +5
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations…
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
Anna Breger, Jose Ignacio Orlando, Pavol Harar +6
The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives i…