A foundation model for generalizable disease detection from retinal images

Foundation models for generalist medical artificial intelligence

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Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging

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Large language models encode clinical knowledge

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AI in health and medicine

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AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK

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Retinal vessel diameters and function in cardiovascular risk and disease

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PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment

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AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline

(2022) Yukun Zhou et al.   Translational Vision Science & Technology

Self-supervised learning in medicine and healthcare

(2022) Rayan Krishnan et al.   Nature Biomedical Engineering

Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

(2022) Ekin Tiu et al.   Nature Biomedical Engineering

Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

(2021) Ling-Ping Cen et al.   Nature Communications

Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning

(2021) Philippe Burlina et al.   JAMA Ophthalmology

Preparing Medical Imaging Data for Machine Learning

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Insights into Systemic Disease through Retinal Imaging-Based Oculomics

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Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection

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A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

(2020) Carol Y. Cheung et al.   Nature Biomedical Engineering

Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis

(2020) Xiaomeng Li et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey

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Impaired Retinal Vessel Dilation Predicts Mortality in End-Stage Renal Disease

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OCTID: Optical coherence tomography image database

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IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

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Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

(2018) Daniel S. Kermany et al.   CELL

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy

(2018) Jonathan Krause et al.   OPHTHALMOLOGY

Association of Retinal Neurodegeneration on Optical Coherence Tomography With Dementia

(2018) Unal Mutlu et al.   JAMA Neurology

Association of Retinal Nerve Fiber Layer Thinning With Current and Future Cognitive Decline

(2018) Fang Ko et al.   JAMA Neurology

Clinically applicable deep learning for diagnosis and referral in retinal disease

(2018) Jeffrey De Fauw et al.   NATURE MEDICINE

The UK Biobank resource with deep phenotyping and genomic data

(2018) Clare Bycroft et al.   NATURE

High-performance medicine: the convergence of human and artificial intelligence

(2018) Eric J. Topol   NATURE MEDICINE

A deep learning model for the detection of both advanced and early glaucoma using fundus photography

(2018) Jin Mo Ahn et al.   PLoS One

Artificial intelligence in healthcare

(2018) Kun-Hsing Yu et al.   Nature Biomedical Engineering

Dermatologist-level classification of skin cancer with deep neural networks

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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

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FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE

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Meta-analysis: Retinal Vessel Caliber and Risk for Coronary Heart Disease

(2013) Kevin McGeechan   ANNALS OF INTERNAL MEDICINE

Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy

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Effects of Age on Optical Coherence Tomography Measurements of Healthy Retinal Nerve Fiber Layer, Macula, and Optic Nerve Head

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