ArticleNature communications2025
Training a high-performance retinal foundation model with half-the-data and 400 times less compute.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Segment any tumour: an uncertainty-aware vision foundation model for whole-body analysis.Nature communications · 2026Article
- Toward Reliable Diabetic Retinopathy Screening.Sensors (Basel, Switzerland) · 2026Article
- Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.Annals of biomedical engineering · 2026Review
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.Korean journal of anesthesiology · 2026Article
- A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.Insights into imaging · 2026Article
- Considering the missing science of retraining and maintenance in medical artificial intelligence, using ophthalmology as an exemplar.NPJ digital medicine · 2026Review
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
- Multimodal Deep Learning for Diabetic Retinopathy: A Survey.Journal of ophthalmology · 2026Review
- Multi-phase deep learning model for automated disease classification from cardiac cine MRI.Journal of the Royal Society, Interface · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Medical artificial intelligence is limited by available training datasets. Foundation models like RETFound from Moorfields Eye Hospital (MEH) can be adapted with small downstream datasets and thus alleviate this issue. RETFound-MEH used 900,000 training images. Recently, "data-efficient" DERETFound achieved comparable performance with 150,000 images. Both require very substantial compute resources for training and use. We propose RETFound-Green trained on only 75,000 publicly available images with 400 times less compute using a novel Token Reconstruction objective. RETFound-MEH and DERETFound training costs are estimated at $10,000 and $14,000, respectively. RETFound-Green cost less than $100, with equally reduced environmental impact. RETFound-Green can be downloaded 14 times faster, computes vector embeddings 2.7 times faster which then require 2.6 times less storage space. On a variety of downstream tasks from geographically diverse datasets, RETFound-Green achieves more than twice as many statistically significant wins than the next best model.
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