5 citations · 12 across the 9 of their papers we have counts for
9 papers
From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs
Omer Nahum, Niv Nayman, Jonathan Fhima +4
A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the model's knowledge. In languag…
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…
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
Jonathan Fhima, Jan Van Eijgen, Lennert Beeckmans +6
Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability. To address this, we propose Retinal Layout-Aware Diffusion (…
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…
TAP-VL: Text Layout-Aware Pre-training for Enriched Vision-Language Models
Jonathan Fhima, Elad Ben Avraham, Oren Nuriel +4
Vision-Language (VL) models have garnered considerable research interest; however, they still face challenges in effectively handling text within images. To address this limitation…
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…
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations
Jonathan Fhima, Jan Van Eijgen, Moti Freiman +2
Introduction: For supervised deep learning (DL) tasks, researchers need a large annotated dataset. In medical data science, one of the major limitations to develop DL models is the…
PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation
Jonathan Fhima, Jan Van Eijgen, Ingeborg Stalmans +3
Introduction: Blood vessels can be non-invasively visualized from a digital fundus image (DFI). Several studies have shown an association between cardiovascular risk and vascular f…