Author

Ching-Yu Cheng

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Open access Aug 2026

Performance and label efficiency of traditional deep-learning models and a retina-specific foundation model for ocular and systemic disease detection: a retrospective comparative study.

For systemic diseases, RETFound consistently outperformed traditional deep-learning models in internal testing when fine-tuned on smaller datasets and with smaller datasets, except in the case of diabetic retinopathy and glaucoma.

S. Yew, Xiao-Feng Lei, Jocelyn Hui Lin Goh et al. · The Lancet Digital Health · 1 citation

#artificial intelligence Preprint Sep 2026

Detecting Glaucoma Across Multi-ethnic Myopic and Non-Myopic Populations Using an Uncertainty-Aware Vision Transformer: A Multicentre Model Development and Validation Study

A Vision Transformer-based deep learning model for glaucoma detection across multi-ethnic cohorts with and without HM showed robust glaucoma detection across myopic and non-myopic multi-ethnic populations and may support AI-assisted screening in settings with high HM prevalence.

R. Lavanya, Yang-Qin Feng, Ten Cheer Quek et al. · 0 citations

Open access Jul 2026

MerMED-FM: Multimodal, Multi-Disease Medical Imaging Foundation Model.

Multimodal, Multi-Disease Medical Imaging Foundation Model MerMED-FM has the potential to be a highly adaptable, versatile, cross-specialty foundation model that enables robust interpretation of medical imaging across diverse medical disciplines.

Yang Zhou, C. Quek, Jun Zhou et al. · The Lancet Digital Health · 2 citations