Search Results for author: José Ignacio Orlando

Found 10 papers, 1 papers with code

SketchZooms: Deep multi-view descriptors for matching line drawings

no code implementations29 Nov 2019 Pablo Navarro, José Ignacio Orlando, Claudio Delrieux, Emmanuel Iarussi

Finding point-wise correspondences between images is a long-standing problem in image analysis.

An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans

no code implementations2 Aug 2019 José Ignacio Orlando, Anna Breger, Hrvoje Bogunović, Sophie Riedl, Bianca S. Gerendas, Martin Ehler, Ursula Schmidt-Erfurth

Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions.

Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography

no code implementations18 Jun 2019 Rhona Asgari, José Ignacio Orlando, Sebastian Waldstein, Ferdinand Schlanitz, Magdalena Baratsits, Ursula Schmidt-Erfurth, Hrvoje Bogunović

We also introduce connections between each class-specific branch and the additional decoder to increase the regularization effect of this surrogate task.

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

no code implementations29 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.

Anomaly Detection

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

no code implementations24 Jan 2019 Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth

Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability.

An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images

1 code implementation9 Jun 2017 José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno, Matthew B. Blaschko

In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge.

Arabidopsis roots segmentation based on morphological operations and CRFs

no code implementations25 Apr 2017 José Ignacio Orlando, Hugo Luis Manterola, Enzo Ferrante, Federico Ariel

Arabidopsis thaliana is a plant species widely utilized by scientists to estimate the impact of genetic differences in root morphological features.

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