858 citations · 1.2k across the 17 of their papers we have counts for
22 papers
End-to-end plaque counting and virus titration from laboratory plate images with deep learning
Eugenia Moris, Alicia Costábile, Sebastián Rey +11
Plaque assays remain the gold standard readout of virus infectivity; however, plaque counting from plate images is labor-intensive and prone to inter-operator variability. We prese…
Evaluating Fundus-Specific Foundation Models for Diabetic Macular Edema Detection
Franco Javier Arellano, José Ignacio Orlando
Diabetic Macular Edema (DME) is a leading cause of vision loss among patients with Diabetic Retinopathy (DR). While deep learning has shown promising results for automatically dete…
Learning normal asymmetry representations for homologous brain structures
Duilio Deangeli, Emmanuel Iarussi, Juan Pablo Princich +3
Although normal homologous brain structures are approximately symmetrical by definition, they also have shape differences due to e.g. natural ageing. On the other hand, neurodegene…
PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Huihui Fang, Fei Li, Junde Wu +6
Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associa…
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
Tomás Castilla, Marcela S. Martínez, Mercedes Leguía +2
Deep learning is currently the state-of-the-art for automated detection of referable diabetic retinopathy (DR) from color fundus photographs (CFP). While the general interest is pu…
A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations
Camila García, Yibin Fang, Jianmin Liu +3
Segmentation of brain arterio-venous malformations (bAVMs) in 3D rotational angiographies (3DRA) is still an open problem in the literature, with high relevance for clinical practi…
Assessing Coarse-to-Fine Deep Learning Models for Optic Disc and Cup Segmentation in Fundus Images
Eugenia Moris, Nicolás Dazeo, Maria Paula Albina de Rueda +7
Automated optic disc (OD) and optic cup (OC) segmentation in fundus images is relevant to efficiently measure the vertical cup-to-disc ratio (vCDR), a biomarker commonly used in op…
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Huihui Fang, Fei Li, Junde Wu +25
With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there ar…
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
Huihui Fang, Fei Li, Huazhu Fu +28
Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss cause…
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
Junde Wu, Huihui Fang, Fei Li +26
Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarker…
AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
Huazhu Fu, Fei Li, Xu Sun +22
Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevat…
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…
Towards a glaucoma risk index based on simulated hemodynamics from fundus images
José Ignacio Orlando, João Barbosa Breda, Karel van Keer +3
Glaucoma is the leading cause of irreversible but preventable blindness in the world. Its major treatable risk factor is the intra-ocular pressure, although other biomarkers are be…
An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images
José Ignacio Orlando, Elena Prokofyeva, Mariana del Fresno +1
Diabetic retinopathy is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms and hemor…
Arabidopsis roots segmentation based on morphological operations and CRFs
José Ignacio Orlando, Hugo Luis Manterola, Enzo Ferrante +1
Arabidopsis thaliana is a plant species widely utilized by scientists to estimate the impact of genetic differences in root morphological features. For this purpose, images of this…