858 citations · 1.2k across the 17 of their papers we have counts for
8 papers · 1 filter
SketchZooms: Deep multi-view descriptors for matching line drawings
Pablo Navarro, José Ignacio Orlando, Claudio Delrieux +1
Finding point-wise correspondences between images is a long-standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature o…
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
José Ignacio Orlando, Huazhu Fu, João Barbossa Breda +28
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modali…
An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
José Ignacio Orlando, Anna Breger, Hrvoje Bogunović +4
Segmenting anatomical structures such as the photoreceptor layer in retinal optical coherence tomography (OCT) scans is challenging in pathological scenarios. Supervised deep learn…
Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography
Rhona Asgari, José Ignacio Orlando, Sebastian Waldstein +4
Automated drusen segmentation in retinal optical coherence tomography (OCT) scans is relevant for understanding age-related macular degeneration (AMD) risk and progression. This ta…
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…
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein +5
Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of…
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…