Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis

[2608.15195] Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.15195 (cs) [Submitted on 15 Aug 2026] Title: Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis Authors: Lovre Antonio Budimir , Mingya Alexa Gong , Alyssa Foong Quinney , Ivana Matovinović , Yukun Zhou , Pearse A. Keane , Sven Lončarić , Marinko V. Šarunić View a PDF of the paper titled Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis, by Lovre Antonio Budimir and 7 other authors View PDF HTML (experimental) Abstract: Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently adapted across diverse imaging modalities, anatomical regions, and clinical tasks. However, VFMs require extensive training data, and their progress in medical image analysis is constrained by limited data availability, privacy concerns, and high development costs. To alleviate these constraints, medical VFMs (MedVFMs) are often built upon weights from generalist models pretrained on vast amounts of publicly available natural images, introducing a substantial distribution shift for medical task adaptation. To address this, we propose satellite imagery as a novel pretraining domain for MedVFM development and benchmarking, motivated by its closer visual alignment with medical data and its freedom from the privacy constraints that limit medical datasets. Across multiple ophthalmic imaging modalities, we compare DINOv3-SAT493m pretrained on 493 million satellite images against DINOv3-LVD1689m pretrained on 1.7 billion natural images, together with two medical specialist baselines: DINOv3-RETFound and MAE-RETFound. Our experiments show that satellite imagery is a stronger pretraining source than natural images for ophthalmic tasks, particularly on en face vascular-rich modalities. On several tasks, satellite pretraining matches or exceeds the medical specialists on high-resolution en face inputs, despite using no medical data. Comments: Accepted at the ECCV 2026 Workshop on Medical Foundation Models and Benchmarks (MEDFMB) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.15195 [cs.CV] (or arXiv:2608.15195v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.15195 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Lovre Antonio Budimir [ view email ] [v1] Sat, 15 Aug 2026 12:23:30 UTC (6,248 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis, by Lovre Antonio Budimir and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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