858 citations · 1.2k across the 17 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…