22 citations · 39 across the 14 of their papers we have counts for
5 papers · 1 filter
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection
Benjamin A. Cohen, Jonathan Fhima, Meishar Meisel +4
Self-supervised learning (SSL) has enabled Vision Transformers (ViTs) to learn robust representations from large-scale natural image datasets, enhancing their generalization across…
HYAMD High-Resolution Fundus Image Dataset for age related macular degeneration (AMD) Diagnosis
Meishar Meisel, Benjamin A. Cohen, Meital Baskin +3
The Hillel Yaffe Age Related Macular Degeneration (HYAMD) dataset is a longitudinal collection of 1,560 Digital Fundus Images (DFIs) from 325 patients examined at the Hillel Yaffe…
GONet: A Generalizable Deep Learning Model for Glaucoma Detection
Or Abramovich, Hadas Pizem, Jonathan Fhima +8
Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The traditional diagnostic approach f…
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images
Yevgeniy Men, Jonathan Fhima, Leo Anthony Celi +3
Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss. Timely identification is critical to curb vision impairment. Al…
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
Jonathan Fhima, Jan Van Eijgen, Hana Kulenovic +7
The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distri…