ArticleScientific reports2024
Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- A Hierarchical Deep Learning Architecture for Diagnosing Retinal Diseases Using Cross-Modal OCT to Fundus Translation in the Lack of Paired Data.Journal of imaging · 2026Article
- Cyclotorsion as a structural and functional biomarker of ocular and neuroophthalmic diseases.Acta biochimica Polonica · 2026Review
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.Scientific reports · 2025Article
- Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis.NPJ digital medicine · 2025Article
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
- Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
- Diagnostic performance and generalizability of deep learning for multiple retinal diseases using bimodal imaging of fundus photography and optical coherence tomography.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Self-supervised learning has become the cornerstone of building generalizable and transferable artificial intelligence systems in medical imaging. In particular, contrastive representation learning techniques trained on large multi-modal datasets have demonstrated impressive capabilities of producing highly transferable representations for different downstream tasks. In ophthalmology, large multi-modal datasets are abundantly available and conveniently accessible as modern retinal imaging scanners acquire both 2D fundus images and 3D optical coherence tomography (OCT) scans to assess the eye. In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-training on 153,306 scan pairs, we show that such a pre-training framework can provide both a retrieval system and encoders that produce comprehensive OCT and fundus image representations that generalize well for various downstream tasks on three independent external datasets, explicitly focusing on clinically pertinent prediction tasks. In addition, we show that interchanging OCT with lower-cost fundus imaging can preserve the predictive power of the trained models.
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Registered trials
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