3 papers
eess.IV2025
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…
cs.CV2025
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 (…
cs.CV2024
Does Data-Efficient Generalization Exacerbate Bias in Foundation Models?
Dilermando Queiroz, Anderson Carlos, MaÃra Fatoretto +3
Foundation models have emerged as robust models with label efficiency in diverse domains. In medical imaging, these models contribute to the advancement of medical diagnoses due to…